Patent Application
20230136842
The present invention relates generally to machine learning, and more specifically, to methods and systems for composing an efficient training data sequence for a recurrent neural network transducer (RNN-T) based global English model.
End-to-end models for automatic speech recognition (ASR) have gained popularity in recent years as a way to fold separate components of a conventional ASR system (e.g., acoustic, pronunciation and language models) into a single neural network. Examples of such models include connectionist temporal classification (CTC) based models, recurrent neural network transducer (RNN-T), and attention-based seq2seq models. Among these models, RNN-T is the most suitable streaming end-to-end recognizer, which has shown competitive performance compared to conventional systems.
Before delving into RNN-T, speech recognition continues to evolve to meet the untethered and nimble demands of a mobile environment. New speech recognition architectures or improvements to existing architectures continue to be developed that seek to increase the quality of ASR systems. To illustrate, speech recognition initially employed multiple models where each model had a dedicated purpose. For instance, an ASR system included an acoustic model (AM), a pronunciation model (PM), and a language model (LM). The acoustic model mapped segments of audio (e.g., frames of audio) to phonemes. The pronunciation model connected these phonemes together to form words while the language model was used to express the likelihood of given phrases (e.g., the probability of a sequence of words). Yet although these individual models worked together, each model was trained independently and often manually designed on different datasets.
The approach of separate models enabled a speech recognition system to be fairly accurate, especially when the training corpus (e.g., body of training data) for a given model caters to the effectiveness of the model, but needing to independently train separate models introduced its own complexities and led to an architecture with integrated models. These integrated models sought to use a single neural network to directly map an audio waveform (e.g., input sequence) to an output sentence (e.g., output sequence). This resulted in a sequence-to-sequence approach, which generated a sequence of words (or graphemes) when given a sequence of audio features. Examples of sequence-to-sequence models include “attention-based” models and “listen-attend-spell” (LAS) models. A LAS model transcribes speech utterances into characters using a listener component, an attender component, and a speller component. Here, the listener is a recurrent neural network (RNN) encoder that receives an audio input (e.g., a time-frequency representation of speech input) and maps the audio input to a higher-level feature representation. The attender attends to the higher-level feature to learn an alignment between input features and predicted subword units (e.g., a grapheme or a word piece). The speller is an attention-based RNN decoder that generates character sequences from the input by producing a probability distribution over a set of hypothesized words. With an integrated structure, all components of a model may be trained jointly as a single end-to-end (E2E) neural network. Here, an E2E model refers to a model whose architecture is constructed entirely of a neural network. A fully neural network functions without external and/or manually designed components (e.g., finite state transducers, a lexicon, or text normalization modules). Additionally, when training E2E models, these models generally do not require bootstrapping from decision trees or time alignments from a separate system.
Although early E2E models proved accurate and a training improvement over individually trained models, these E2E models, such as the LAS model, functioned by reviewing an entire input sequence before generating output text, and thus, did not allow streaming outputs as inputs were received. Without streaming capabilities, an LAS model is unable to perform real-time voice transcription. Due to this deficiency, deploying the LAS model for speech applications that are latency sensitive or require real-time voice transcription may pose issues.
Additionally, speech recognition systems that have acoustic, pronunciation, and language models, or such models composed together, may rely on a decoder that has to search a relatively large search graph associated with these models. With a large search graph, it is not conducive to host this type of speech recognition system entirely on-device. Here, when a speech recognition system is hosted “on-device,” a device that receives the audio input uses its processor(s) to execute the functionality of the speech recognition system. For instance, when a speech recognition system is hosted entirely on-device, the processors of the device do not need to coordinate with any off-device computing resources to perform the functionality of the speech recognition system. A device that performs speech recognition not entirely on-device relies on remote computing (e.g., of a remote computing system or cloud computing) and therefore online connectivity to perform at least some function of the speech recognition system. For example, a speech recognition system performs decoding with a large search graph using a network connection with a server-based model.
Unfortunately, being reliant upon a remote connection makes a speech recognition system vulnerable to latency issues and/or inherent unreliability of communication networks. To improve the usefulness of speech recognition by avoiding these issues, speech recognition systems again evolved into a form of a sequence-to-sequence model known as a recurrent neural network transducer (RNN-T). A RNN-T does not employ an attention mechanism and, unlike other sequence-to-sequence models that generally need to process an entire sequence (e.g., audio waveform) to produce an output (e.g., a sentence), the RNN-T continuously processes input samples and streams output symbols, a feature that is particularly attractive for real-time communication. For instance, speech recognition with an RNN-T may output characters one-by-one as spoken.
Accordingly, a need exists for more efficient processes for training data sequences using RNN-T.
In accordance with an embodiment, a computer-implemented method for preparing training data for a speech recognition model is provided. The computer-implemented method includes obtaining a plurality of audio data sets, each audio data set having a different acoustic feature and sorting sentences from the plurality of audio data sets so that similar sentences from different audio data sets are positioned closely, while imposing a weak constraint on audio length, to train the speech recognition model.
In accordance with another embodiment, a computer program product for preparing training data for a speech recognition model is provided. The computer program product includes a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to obtain a plurality of audio data sets, each audio data set having a different acoustic feature and sort sentences from the plurality of audio data sets so that similar sentences from different audio data sets are positioned closely, while imposing a weak constraint on audio length, to train the speech recognition model.
In accordance with yet another embodiment, a system for preparing training data for a speech recognition model is provided. The system includes a memory and one or more processors in communication with the memory configured to obtain a plurality of audio data sets, each audio data set having a different acoustic feature and sort sentences from the plurality of audio data sets so that similar sentences from different audio data sets are positioned closely, while imposing a weak constraint on audio length, to train the speech recognition model.
In accordance with another embodiment, a computer-implemented method for preparing training data for a speech recognition model is provided. The computer-implemented method includes obtaining a plurality of audio data sets, each audio data set having a different acoustic feature, sorting sentences from the plurality of audio data sets so that similar sentences from different audio data sets are positioned closely to train the speech recognition model, and grouping the similar sentences from the different audio data sets into mini-batches, wherein each mini-batch of the mini-batches includes sentence pairs between different English dialects.
In accordance with yet another embodiment, a computer program product for preparing training data for a speech recognition model is provided. The computer program product includes a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computer to cause the computer to obtain a plurality of audio data sets, each audio data set having a different acoustic feature, sort sentences from the plurality of audio data sets so that similar sentences from different audio data sets are positioned closely to train the speech recognition model, and group the similar sentences from the different audio data sets into mini-batches, wherein each mini-batch of the mini-batches includes sentence pairs between different English dialects.
In one preferred aspect, the plurality of audio data sets are sampled from data pools each having the different acoustic feature so that the sampled audio data sets include a plurality of sets of similar sentences.
In another preferred aspect, a score penalty is presented to control a variety of sentences.
In yet another preferred aspect, the similar sentences are similar sentences with different dialects of a target language.
In yet another preferred aspect, the speech recognition model is a global speech recognition model for the target language.
In yet another preferred aspect, the similar sentences from the different audio data sets are grouped into mini-batches.
In yet another preferred aspect, each mini-batch of the mini-batches includes sentence pairs between different English dialects.
In yet another preferred aspect, each mini-batch of the mini-batches includes a similar amount of dialect data.
In yet another preferred aspect, a similarity between different English dialects of the similar sentences from different audio data sets is given by:
where F(a, b) is a distance between sentences a and b based on a word vector of n-word sequences and P(d) is a similarity-score dependent penalty not to compose biased text data.
In yet another preferred aspect, the similarity-score dependent penalty is given by:
P(d)=γeκd−γ(d>0), where γ, κ are hyper parameters.
It should be noted that the exemplary embodiments are described with reference to different subject-matters. In particular, some embodiments are described with reference to method type claims whereas other embodiments have been described with reference to apparatus type claims. However, a person skilled in the art will gather from the above and the following description that, unless otherwise notified, in addition to any combination of features belonging to one type of subject-matter, also any combination between features relating to different subject-matters, in particular, between features of the method type claims, and features of the apparatus type claims, is considered as to be described within this document.
These and other features and advantages will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.
The invention will provide details in the following description of preferred embodiments with reference to the following figures wherein:
Throughout the drawings, same or similar reference numerals represent the same or similar elements.
Embodiments in accordance with the present invention provide methods and devices for composing an efficient training data sequence for a recurrent neural network transducer (RNN-T) based global English model. RNN-T models are usually trained with RNN-T loss, which aims to improve the log-likelihood of training data. However, few research work has investigated sequential training criteria for RNN-T models.
In the present circumstances, RNN-T models that are specific to each language are constructed separately. Even for the English language, several models are created independently to achieve a sufficient performance as a practical service, because of strong dialects (accents) in each English-speaking country. For example, American English (US), Australian English (AU), and British English (UK) models are currently deployed as an individual language. From the viewpoint of usability and maintenance cost, it is however practical to construct and deploy a single unified English model (referred to herein as a global English model (GEM)), which processes multiple English dialects with a single model. One beneficial aspect for GEM construction is to compose an efficient training data including multiple dialects with a good balance in terms of data size. Usually, those data sets are imbalanced.
The exemplary embodiments of the present invention alleviate such issues by introducing a method that advantageously does better training data collection (sorting and sampling) for accurate global English model construction.
It is to be understood that the present invention will be described in terms of a given illustrative architecture; however, other architectures, structures, substrate materials and process features and steps/blocks can be varied within the scope of the present invention. It should be noted that certain features cannot be shown in all figures for the sake of clarity. This is not intended to be interpreted as a limitation of any particular embodiment, or illustration, or scope of the claims.
In a conventional method 5, the data 10 is provided for random sampling 12, data sorting 14 based on audio length, and model training 16.
The data 10 can be, e.g., Australian English text or voice messages, British English text or voice messages, and American English text or voice messages.
In contrast, the exemplary embodiments introduce a method 20 where the data 10 is advantageously provided to a data sampler 22 for data sampling based on sentence similarity between dialects, then to a data sorter 24 for data sorting based on sentence similarity between dialects, and then model training 26 is performed.
The data 10 can be, e.g., Australian English text or voice messages, British English text or voice messages, and American English text or voice messages.
Thus, method 20 organizes a training data sequence based on a metric that similar sentences with different dialects are advantageously positioned closely with a weak constraint of audio length for global English model (GEM) construction. An advantage is that each mini-batch includes similar sentence pairs between different English dialects. Each mini-batch advantageously includes a similar-amount of dialect data. The same metric can also be applied to data sampling from a large data pool including realistic field data such as customer data of IBM Watson® speech to text (STT) for better GEM construction. How realistic big data can be efficiently leveraged to be organized by better training data of GEM is an advantage of this invention. A beneficial aspect for the data sampling is to introduce a score penalty to control a variety of sentences. This makes a prediction network training not to be overfitted to biased text caused by a strong word-sequence constraint.
IBM Watson® STT technology enables fast and accurate speech transcription in multiple languages for a variety of use cases, including, but not limited to, customer self-service, agent assistance, and speech analytics. IBM Watson® STT is an application programming interface (API) cloud service that enables a person to convert written text into natural sounding audio in a variety of languages and voices within an existing application on, e.g., Watson® assistant.
At block 30, a subset DBS of the base dialect is made by randomly selecting utterances from DB.
At block 32, DBSL is made by sorting utterances in DBS with a metric of audio length.
At block 34, an unprocessed shortest utterance SiBSL is selected from DBSL.
At block 36, set n=1.
At block 38, an utterance with the highest similarity is extracted from other dialect Dn as a better training sample for the GEM construction.
At block 40, it is determined whether n=N. If NO, then proceed to block 42, where n is set to n+1. If YES, then proceed to block 44.
At block 44, it is determined whether all utterances in DBSL have been proceed. If NO, then proceed back to block 34. If YES, then the process ends.
DB is the training data set of the base dialect.
The smallest amount dialect training data is used as the base set, but not limited to this.
Dn is the training data sets of other dialects. (n=1 . . . N, where N is the number of other dialects).
The similarity between dialects is advantageously given as:
Where F(a, b) is the distance between sentences a and b based on word vector of n-word sequences and P(d) is a similarity-score dependent penalty not to compose biased text data.
P(d) is advantageously given by:
P(d)=γeκd−γ(d>0)
Where γ, κ are hyper parameters.
At block 50, obtain a plurality of audio data sets, each audio data set having a different acoustic feature.
At block 52, advantageously sort sentences from the plurality of audio data sets so that similar sentences from different audio data sets are positioned closely, while imposing a weak constraint on audio length, to train the speech recognition model.
Moreover, the plurality of audio data sets are advantageously sampled from data pools each having the different acoustic feature so that the sampled audio data sets include a plurality of sets of similar sentences, while presenting a score penalty to control a variety of sentences. Additionally, the similar sentences are similar sentences with different dialects of a target language and the speech recognition model is a global speech recognition model for the target language.
Block 60 illustrates the random selection from each dialect.
The first few sentences are in Australian English (au), the next few sentences are in British English (uk), and the last few sentences are in American English (us).
Thus, the sentences are grouped by what type of English they are (e.g., au, uk, us), regardless of the words within the sentences or the length of the sentences or any other characteristics of the sentences.
Block 70 illustrates sentences sorted by audio length.
The first sentence (THANK YOU) is the shortest and listed at the top, whereas the last sentence (OKAY I JUST WANTED TO ASK YOU TO STAY ON THE LINE FOR A MEMOMET WE ARE HERE UNTIL NINE P M) is the longest and listed at the bottom.
Thus, the sentences are listed by length, regardless of any other factors.
Block 80, according to the exemplary embodiments, advantageously sorts the sentences by similarity.
For example, the first group 82 includes 3 sentences that include the phrase “THANK YOU.” Since, the phrase “THANK YOU” is found in all 3 sentences, regardless of dialect or length, such sentences are grouped together as 82 (based only on similarity).
The second group 84 also includes 3 sentences. Each sentence includes the phrase “I WILL RING.” Since, the phrase “I WILL RING” is found in all 3 sentences, regardless of dialect or length, such sentences are grouped together as 84 (based only on similarity).
The third group 86 also includes 3 sentences. Each sentence includes the phrase “WANTS TO DO” or “WANTS TO KNOW.” Since, such phrases are similar and found in all 3 sentences, regardless of dialect or length, such sentences are grouped together as 86 (based only on similarity).
The fourth group 88 also includes 3 sentences. Each sentence includes the phrase “OKAY,” such as “OKAY WE'RE HERE” or “I AH OKAY USED” or “OKAY I JUST WANTED TO ASK.” Since, such phrases are similar and found in all 3 sentences, regardless of dialect or length, such sentences are grouped together as 88 (based only on similarity).
Therefore, similar sentences with different dialects are advantageously put close to each other, and thus grouped together (e.g., in mini-batches). In other words, the closeness or similarity of the words or phrases is analyzed and evaluated to determine groupings or mini-batches. Each group 82, 84, 86, 88 can be referred to as a mini-batch. Mini-batches can include, e.g., 3 sentences. However, mini-batches can include anywhere from 3 to 10 sentences.
In one example, a first audio data set 90 is obtained having an acoustic feature 92, a second audio data set 100 is obtained having an acoustic feature 102, and a third audio data set 110 is obtained having an acoustic feature 112. Sentences from the audio datasets 90, 100, 110 are advantageously sorted, by a sorter 115, for similarity or closeness to efficiently train the speech recognition model 120. The similar sentences can be grouped into a plurality of mini-batches, as described above with reference to
Moreover, a weak constraint is imposed on the audio length, thus advantageously making the similarity feature or variable or parameter more dominant in determining the mini-batches.
Computing device 205 includes communications fabric 202, which provides communications between computer processor(s) 204, memory 206, persistent storage 208, communications unit 210, and input/output (I/O) interface(s) 212. Communications fabric 202 can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric 202 can be implemented with one or more buses.
Memory 206, cache memory 216, and persistent storage 208 are computer readable storage media. In this embodiment, memory 206 includes random access memory (RAM) 214. In another embodiment, the memory 206 can be flash memory. In general, memory 206 can include any suitable volatile or non-volatile computer readable storage media.
In some embodiments of the present invention, program 225 is included and operated by AI accelerator chip 222 as a component of computing device 205. In other embodiments, program 225 is stored in persistent storage 208 for execution by AI accelerator chip 222 (to implement a training data sequence for RNN-T) in conjunction with one or more of the respective computer processors 204 via one or more memories of memory 206. In this embodiment, persistent storage 208 includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage 208 can include a solid state hard drive, a semiconductor storage device, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information.
The media used by persistent storage 208 can also be removable. For example, a removable hard drive can be used for persistent storage 208. Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage 208.
Communications unit 210, in these examples, provides for communications with other data processing systems or devices, including resources of distributed data processing environment. In these examples, communications unit 210 includes one or more network interface cards. Communications unit 210 can provide communications through the use of either or both physical and wireless communications links. Deep learning program 225 can be downloaded to persistent storage 208 through communications unit 210.
I/O interface(s) 212 allows for input and output of data with other devices that can be connected to computing system 200. For example, I/O interface 212 can provide a connection to external devices 218 such as a keyboard, keypad, a touch screen, and/or some other suitable input device. External devices 218 can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards.
Display 220 provides a mechanism to display data to a user and can be, for example, a computer monitor.
It is to be understood that although this invention includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model can include at least five characteristics, at least three service models, and at least four deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.
Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but can be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).
Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.
Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an organization. It can be managed by the organization or a third party and can exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It can be managed by the organizations or a third party and can exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.
Referring now to
Hardware and software layer 560 includes hardware and software components. Examples of hardware components include: mainframes 561; RISC (Reduced Instruction Set Computer) architecture based servers 562; servers 563; blade servers 564; storage devices 565; and networks and networking components 566. In some embodiments, software components include network application server software 567 and database software 568.
Virtualization layer 570 provides an abstraction layer from which the following examples of virtual entities can be provided: virtual servers 571; virtual storage 572; virtual networks 573, including virtual private networks; virtual applications and operating systems 574; and virtual clients 575.
In one example, management layer 580 can provide the functions described below. Resource provisioning 581 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 582 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources can include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 583 provides access to the cloud computing environment for consumers and system administrators. Service level management 584 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 585 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.
Workloads layer 590 provides examples of functionality for which the cloud computing environment can be utilized. Examples of workloads and functions which can be provided from this layer include: mapping and navigation 541; software development and lifecycle management 592; virtual classroom education delivery 593; data analytics processing 594; transaction processing 595; and training data sequence for RNN-T 20.
The present invention can be a system, a method, and/or a computer program product. The computer program product can include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium can be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory, a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network can include copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the present invention can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, to perform aspects of the present invention.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions can be provided to at least one processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks or modules. These computer readable program instructions can also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein includes an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks or modules.
The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational blocks/steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks or modules.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams can represent a module, segment, or portion of instructions, which includes one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession can, in fact, be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
Reference in the specification to “one embodiment” or “an embodiment” of the present principles, as well as other variations thereof, means that a particular feature, structure, characteristic, and so forth described in connection with the embodiment is included in at least one embodiment of the present principles. Thus, the appearances of the phrase “in one embodiment” or “in an embodiment”, as well any other variations, appearing in various places throughout the specification are not necessarily all referring to the same embodiment.
It is to be appreciated that the use of any of the following “/”, “and/or”, and “at least one of”, for example, in the cases of “A/B”, “A and/or B” and “at least one of A and B”, is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of both options (A and B). As a further example, in the cases of “A, B, and/or C” and “at least one of A, B, and C”, such phrasing is intended to encompass the selection of the first listed option (A) only, or the selection of the second listed option (B) only, or the selection of the third listed option (C) only, or the selection of the first and the second listed options (A and B) only, or the selection of the first and third listed options (A and C) only, or the selection of the second and third listed options (B and C) only, or the selection of all three options (A and B and C). This can be extended, as readily apparent by one of ordinary skill in this and related arts, for as many items listed.
Having described preferred embodiments of methods and systems for composing an efficient training data sequence for a recurrent neural network transducer (RNN-T) based global English model (which are intended to be illustrative and not limiting), it is noted that modifications and variations can be made by persons skilled in the art in light of the above teachings. It is therefore to be understood that changes may be made in the particular embodiments described which are within the scope of the invention as outlined by the appended claims. Having thus described aspects of the invention, with the details and particularity required by the patent laws, what is claimed and desired protected by Letters Patent is set forth in the appended claims.
