Communication System Using Mixed Translating While in Multilingual Communication

Abstract

A translation between a source language and a target language. The items are divided, with secondary source language items or named entities being identified. Those entities are translated in a different way. For example, they may be copied into the target language, or translated in a special way that is based on their meaning, e.g., into a term that has a more descriptive meaning in the target language.

Description

BACKGROUND

Speech recognition and speech translation systems receive spoken information in one language, and translate it to another language. These systems are often based on a database that has been trained using the two different languages.

SUMMARY

The present application teaches techniques for handling mixed multilingual communication in a speech recognition and translation system.

According to an embodiment, entities from outside the source language are isolated and preserved during machine translation of speech.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other aspects will now be discussed in reference to the accompanying drawings, wherein:

FIG. 1 illustrates an embodiment where a computer runs a program that is stored on the storage media; and

FIG. 2 illustrates a flowchart of some preprocessing parts that are carried out.

DETAILED DESCRIPTION

The general structure and techniques, and more specific embodiments, which can be used to effect different ways of carrying out the more general goals, are described herein.

The present application relates to issues arising in speech recognition and speech translation systems. These systems can use text-to-text translation system, or can be systems that listen to oral communication, e.g. speech translation systems. These systems are referred to herein as being speech systems. It should be understood that speech systems include generically all of these systems.

The inventors recognized a problem that occurs in these systems. Specifically, it is not uncommon to hear multiple different languages uttered together. For example, it is not uncommon to hear an English utterance in a foreign language. The English utterances may be English names, English words, or others. Bilingual speakers may even pronounce the foreign words with the appropriate accent for the multiple languages.

Speech systems have historically assumed that the input speech is in a specified language, and compare it against corpora formed from training information. The attempt to translate may be complicated or fooled by the foreign language words.

An embodiment described herein uses language detection for increased speech recognition and speech translation. An embodiment describes processing and/or preservation of foreign language material within information to be translated.

First, a notation is described. Consider the example of a translation from an image speaker into Arabic. In the example, the English speaker is bilingual, also fluent in Spanish, and may use Spanish words within the utterance. The languages are represented notationally as follows. The primary source language or PL is English in this example, and transcription of text (P) refers to this primary source language. The secondary source language here is Spanish, referred to as SL; transcription of text(S). The target language or TL here is Arabic, and transcription of text (T).

The operation can be carried out by a programmed computer that runs the flowcharts described herein. The computer can be as shown in FIG. 1. FIG. 1 illustrates an embodiment where a computer 100 runs a program that is stored on the storage media 105. The program produces output through a human-computer interface 110 such as a display, sound (loudspeakers, headphones etc) or any other means. The user can interact with the program and display via a user interface which may include a keyboard, microphone, mouse, and any other user interface part materials.

In operation, the computer is programmed to carry out a speech operation. FIG. 2 illustrates a flowchart which may be a preprocessing operation for the final speech operation. These preprocessing parts may be carried out as part of the segmentation of the speech, for example.

At 200, the system first detects words within the language that are “named entities”. The named entities may be in either the primary language or the secondary language. This detection allows the name entities to be appropriately conveyed to the target. Embodiments as described herein use proper names as the named entities; however it should be understood that other named entities can be used.

210 illustrates labeling the named entities as being separately translatable. Certain named entities, such as proper names, may be conveyed or preserved into the translated language either by re-synthesis in the translated language or by simple repetition of the primary language or secondary language after voice morphing into the translated language. In the embodiment, the names can be proper names, can be product names, they can be items which represent places, or they can simply be words in the foreign (primary or secondary source) language representing places such as city names.

The detecting and translating can be carried out as follows.

(my name is John)P becomes

(my name is)P (John)P

Translating this to the target language (P->T) yields

(my name is)T (John)P.

In this example, the word John is actually a named entity, here a name, in the primary language, and needs to stay in the primary language as part of the translation. Accordingly, the phrase is first segmented into the two parts: “my name is” first in the primary language, and “John” also in the primary language. Upon translation, the “my name is” part is translated, but “John” is maintained. The named entity may be uttered via automated speech reading, or alternatively may be recorded in the source language and replayed in the target language.

A second example includes information in both the primary and secondary languages.

(My name is Jorge)P

(My name is)P (Jorge)S

P->T

(My name is)T (Jorge)S.

In both of these examples, the “Jorge” and “John” do not have corresponding translations in the foreign language, and hence the primary language or secondary language word is kept at 230. The remainder is translated at 240.

If the label names have a translation in the foreign language, they are translated at 240. An example is:

(Take this aspirin)P

(Take this)P (aspirin)P

P->T

(Take this) T (aspirin)T.

In this example, the word “aspirin”, which can be in the primary or secondary language has a translation in the foreign language. Accordingly, this translation is used.

Many times, the named entity will also have meaning as a regular dictionary word, but identifying it as a named entity will increase its translation accuracy. For example:

(He is driving an Infinity)P

(He is driving)P (an Infinity)P.

P->T

(He is driving)T (a luxury car)T

This kind of generic word for the meaning of the term in the source language may be more meaningful in the translated language. Alternatively, the original word can be retained. However, in this example, failure to identify the word “Infinity” as a named entity would likely cause it to be mistranslated, e.g., as the number infinity.

Although only a few embodiments have been disclosed in detail above, other embodiments are possible and the inventor (s) intend these to be encompassed within this specification. The specification describes specific examples to accomplish a more general goal that may be accomplished in another way. This disclosure is intended to be exemplary, and the claims are intended to cover any modification or alternative which might be predictable to a person having ordinary skill in the art. For example, while the above has discussed identifying names within the phrases, it should be understood that it is alternatively possible to simply recognize any foreign word in the secondary language within the primary language during its segmentation Also, the inventor(s) intend that only those claims which use the words “means for” are intended to be interpreted under 35 USC 112, sixth paragraph. Moreover, no limitations from the specification are intended to be read into any claims, unless those limitations are expressly included in the claims. The computers described herein may be any kind of computer, either general purpose, or some specific purpose computer such as a workstation. The computer may be an Intel (e.g., Pentium or Core 2 duo) or AMD based computer, running Windows XP or Linux, or may be a Macintosh computer. The computer may also be a handheld computer, such as a PDA, cellphone, or laptop, or any other device such as a game console, a media console etc.

The programs may be written in C, or C++, or Python, or Java, or Brew, or any other programming language. The programs may be resident on a storage medium, e.g., magnetic or optical, e.g. the computer hard drive, a removable disk or media such as a memory stick or SD media, wired or wireless network based or Bluetooth based Network Attached Storage (NAS), or other removable medium. The programs may also be run over a network, for example, with a server or other machine sending signals to the local machine, which allows the local machine to carry out the operations described herein.

Where a specific numerical value is mentioned herein, it should be considered that the value may be increased or decreased by 20%, while still staying within the teachings of the present application, unless some different range is specifically mentioned. Where a specified logical sense is used, the opposite logical sense is also intended to be encompassed.

Claims

1. A method, comprising: receiving information to be translated; identifying at least one part of the information which needs to be translated differently within the information to be translated; and translating the at least one part in a different way.

2. A method as in claim 1, wherein the at least one part is a word representing a named entity.

3. A method as in claim 2, wherein the information includes a primary source language, and said at least one part is a word in a secondary source language.

4. A method as in claim 1, wherein the information includes a primary source language, and said at least one part is a word in a secondary source language.

5. A method as in claim 1, wherein said information to be translated is primarily in a primary source language, but also includes words in a secondary source language, and wherein said identifying comprises identifying words in the secondary source language, and wherein said translating comprises reproducing the words in the secondary language into a target language, without translating.

6. A method as in claim 1, wherein said at least one part includes proper names.

7. A method, comprising: receiving information to be translated in a source language, to be translated to a target language, wherein said information includes a first part that is in a primary source language and a second part that is in a secondary source language; segmenting said information to include a distinction between said primary source language parts and said secondary source language parts; translating said primary source language parts into the target language; and carrying out an operation on the secondary source language that is different than said translating.

8. A method as in claim 7, wherein said operation comprises copying the secondary source language parts into the target language without modification.

9. A method as in claim 7, wherein said operation comprises translating the secondary source language parts using a different translation that is specific for second source language parts.

10. A method as in claim 7, further comprising detecting named entities as parts in said secondary source language.

11. A method as in claim 10, wherein said named entities include proper names.

12. A method as in claim 8, wherein said copying comprises using an automated speech reading part to read the speech into the target language.

13. A method as in claim 8, wherein said copying comprises recording the speech in the source language, and replaying the speech in the target language.

14. A method as in claim 11, wherein said detecting named entities further comprises determining if the named entity has a translation in the target language, and if not then copying the information into the target language.

15. A method as in claim 14, wherein said named entity is a word that has multiple translations into the target language, and has a different translation when used as a named entity than it has when used as a non-named entity.

16. A method, comprising: obtaining information in a source language to be translated; segmenting said information into a first part that represents information in the source language, and a second part that represents named entities in the source language; determining if the named entities in the source language have a different translation into the target language than they would have if non-named entities; and translating said named entities using said different translation.

17. A method as in claim 16, wherein said named entities are in a primary source language.

18. A method as in claim 16, wherein the named entities are in a secondary source language.

19. A computer system, comprising: a first part, that receives information in a source language to be translated into a target language; a processor, programmed to divide the information to be translated into a first part which is transmitted according to a first translation between said source language and said target language, and a second part which is translated in a special way that is different than the first translation, wherein the special way translates the second part in a first case by copying the second part into the target language, and in a second case by translating the second part into the target language, and wherein the processor converts the information from the source language into the target language.

20. A system as in claim 19, wherein said special way translates into a generic term in the target language.