SPEECH RECOGNITION APPARATUS, SPEECH RECOGNITION METHOD, AND ELECTRONIC DEVICE

• US 20170004824A1
• Filed: 04/27/2016
• Published: 01/05/2017
• Est. Priority Date: 06/30/2015
• Status: Abandoned Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: YOO, Sang Hyun, CHOI, Hee Youl
• Application Number:
  US15/139,926
• Publication Number:
  US 20170004824A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 40/44 Statistical methods, e.g. probability models
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G10L 15/00 Speech recognition G10L17/00 takes precedence
• CPC Class Codes: G10L 15/02 Feature extraction for speech recognition; Selection of recognition unit
• CPC Class Codes: G10L 15/187 Phonemic context, e.g. pronunciation rules, phonotactical constraints or phoneme n-grams
• CPC Class Codes: G10L 15/197 Probabilistic grammars, e.g. word n-grams
• CPC Class Codes: G10L 15/22 Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 2015/025 Phonemes, fenemes or fenones being the recognition units
• CPC Class Codes: G10L 2015/228 of application context

Abstract

A speech recognition apparatus includes a probability calculator configured to calculate phoneme probabilities of an audio signal using an acoustic model; a candidate set extractor configured to extract a candidate set from a recognition target list; and a result returner configured to return a recognition result of the audio signal based on the calculated phoneme probabilities and the extracted candidate set.

Claims

1. A speech recognition apparatus comprising:

a probability calculator configured to calculate phoneme probabilities of an audio signal using an acoustic model;

a candidate set extractor configured to extract a candidate set from a recognition target list of target sequences; and

a result returner configured to return a recognition result of the audio signal based on the calculated phoneme probabilities and the extracted candidate set.

2. The apparatus of claim 1, wherein the acoustic model is trained using a learning algorithm comprising Connectionist Temporal Classification (CTC).

3. The apparatus of claim 1, wherein the result returner is further configured to calculate probabilities of generating each target sequence included in the candidate set based on the calculated phoneme probabilities, and return a candidate target sequence having a highest probability among the calculated probabilities of generating each target sequence as the recognition result.

4. The apparatus of claim 1, further comprising a sequence acquirer configured to acquire a phoneme sequence based on the calculated phoneme probabilities.

5. The apparatus of claim 4, wherein the candidate set extractor is further configured to calculate similarities between the acquired phoneme sequence and each target sequence included in the recognition target list, and extract the candidate set based on the calculated similarities.

6. The apparatus of claim 5, wherein the candidate set extractor is further configured to calculate the similarities using a similarity algorithm comprising an edit distance algorithm.

7. The apparatus of claim 4, wherein the sequence acquirer is further configured to acquire the phoneme sequence based on the calculated phoneme probabilities using a best path decoding algorithm or a prefix search decoding algorithm.

8. A speech recognition method comprising:

calculating phoneme probabilities of an audio signal using an acoustic model;

extracting a candidate set from a recognition target list of target sequences; and

returning a recognition result of the audio signal based on the calculated phoneme probabilities and the extracted candidate set.

9. The method of claim 8, wherein the acoustic model is trained using a learning algorithm comprising Connectionist Temporal Classification (CTC).

10. The method of claim 8, wherein the returning of the recognition result comprises:

calculating probabilities of generating each target sequence included in the candidate set based on the calculated phoneme probabilities; and

returning a candidate target sequence having a highest probability among the calculated probabilities of generating each target sequence as the recognition result.

11. The method of claim 8, further comprising acquiring a phoneme sequence based on the calculated phoneme probabilities.

12. The method of claim 11, wherein the extracting of the candidate set comprises:

calculating similarities between the acquired phoneme sequence and each target sequence included in the recognition target list; and

extracting the candidate set based on the calculated similarities.

13. The method of claim 12, wherein the calculating of the similarities comprises calculating the similarities using a similarity algorithm comprising an edit distance algorithm.

14. The method of claim 11, wherein the acquiring of the phoneme sequence comprises acquiring the phoneme sequence based on the calculated phoneme probabilities using a best path decoding algorithm or a prefix search decoding algorithm.

15. An electronic device comprising:

a speech receiver configured to receive an audio signal of a user;

a speech recognizer configured to calculate phoneme probabilities of the received audio signal using an acoustic model, and based on the calculated phoneme probabilities, return any one of target sequences included in a recognition target list as a recognition result; and

a processor configured to perform a specific operation based on the returned recognition result.

16. The electronic device of claim 15, wherein the speech recognizer is further configured to extract a candidate set from the recognition target list, calculate probabilities of generating each candidate target sequence included in the candidate set based on the calculated phoneme probabilities, and return a candidate target sequence having a highest probability among the calculated probabilities of generating each target sequence as the recognition result.

17. The electronic device of claim 15, wherein the speech recognizer is further configured to acquire a phoneme sequence by decoding the phoneme probabilities, and extract the candidate set based on similarities between the acquired phoneme sequence and each target sequence included in the recognition target list.

18. The electronic device of claim 15, wherein the processor is further configured to output the recognition result in a voice from a speaker, or in a text format on a display.

19. The electronic device of claim 18, wherein the processor is further configured to translate the recognition result into another language, and output the translated result in the voice from the speaker, or in the text format on the display.

20. The electronic device of claim 15, wherein the processor is further configured to process commands comprising one or more of a power on/off command, a volume control command, a channel change command, and a destination search command in response to the recognition result.

21. A speech recognition method comprising:

calculating probabilities that portions of an audio signal correspond to speech units;

obtaining a set of candidate sequences of speech units from a list of sequences of speech units; and

recognizing one of the candidate sequences of speech units as corresponding to the audio signal based on the probabilities.

22. The method of claim 21, wherein the calculating of the probabilities comprises calculating the probabilities using an acoustic model.

23. The method of claim 21, wherein the speech units are phonemes.

24. The method of claim 21, wherein the candidate sequences of speech units are phrases.

25. The method of claim 24, wherein the phrases are commands to control an electronic device.

26. The method of claim 21, wherein the recognizing of the one of the candidate sequences of speech units comprises:

calculating probabilities of generating each of the candidate sequences of speech units based on the probabilities that portions of the audio signal correspond to the speech units; and

recognizing one of the candidate sequences of speech units having a highest probability among the probabilities of generating each of the candidate sequences of speech units as corresponding to the audio signal.