Pre-wakeword speech processing with reverse automatic speech recognition
Methods and systems for pre-wakeword speech processing are disclosed. Speech audio, comprising command speech spoken before a wakeword, may be stored in a buffer in oldest to newest order. Upon detection of the wakeword, reverse acoustic models and language models, such as reverse automatic speech recognition (R-ASR) can be applied to the buffered audio, in newest to oldest order, starting from before the wakeword. The speech is converted into a sequence of words. Natural language grammar models, such as natural language understanding (NLU), can be applied to match the sequence of words to a complete command, the complete command being associated with invoking a computer operation.
1. A computer-implemented method of recognizing a command spoken before a wakeword, the method comprising:
receiving an audio signal comprising speech;
storing the audio signal in a sequence of spectral frames in a buffer;
detecting a wakeword in the audio signal;
estimating an initial frame of the wakeword;
converting a plurality of frames into a sequence of words using reverse automatic speech recognition (R-ASR), in newest to oldest order, beginning from a first frame before the initial frame of the wakeword;
matching the sequence of words to a complete command;
identifying a mid-sentence correction between the command and the wakeword, the mid-sentence correction corresponding to words matching a second complete command; and;
invoking a function associated with the second complete command.
2. The computer-implemented method of claim 1 , wherein using R-ASR further comprises referencing at least a phonetic dictionary, the phonetic dictionary having reverse pronunciations of words.
3. The computer-implemented method of claim 1 , wherein using R-ASR further comprises referencing at least a language model, the language model having reverse orders of word sequences.
4. The computer-implemented method of claim 1 , further comprising:
transforming the audio signal into a sequence of reversed phonemes through R-ASR.
5. The computer-implemented method of claim 1 , further comprising:
terminating R-ASR when the sequence of words matches the complete command.
6. The computer-implemented method of claim 1 , further comprising:
estimating a last frame of the wakeword;
converting a second plurality of frames into a second sequence of words using automatic speech recognition (ASR) system, in oldest to newest order, beginning from a first frame after the last frame of the wakeword;
combining the sequence of words and the second sequence of words into a combined sequence of words;
matching the combined sequence of words to the complete command; and
invoking the function associated with the complete command.
7. The computer-implemented method of claim 6 , wherein using ASR further comprises referencing at least a second phonetic dictionary, the second phonetic dictionary having forward pronunciations of words.
8. The computer-implemented method of claim 6 , wherein using ASR further comprises referencing at least a second language model, the second language model having forward orders of word sequences.
9. The computer-implemented method of claim 6 , further comprising:
converting the plurality of frames using R-ASR and converting the second plurality of frames using ASR in separate simultaneous threads.
10. The computer-implemented method of claim 6 , further comprising:
converting the plurality of frames using R-ASR on a high-performance processor; and
converting the second plurality of frames using ASR on a low-performance processor.
11. The computer-implemented method of claim 1 , further comprising:
detecting a pause in the audio signal; and
converting the plurality of frames using R-ASR, in newest to oldest order, beginning from the first frame before the initial frame of the wakeword toward the pause.
12. The computer-implemented method of claim 1 , wherein the wakeword is a high frequency phrase.
13. A computer-implemented method of recognizing a command, the method comprising:
receiving an audio signal comprising speech;
detecting a wakeword in the audio signal;
estimating a beginning time of the wakeword;
converting the audio signal into a sequence of words using reverse automatic speech recognition (R-ASR), in newest to oldest order, from before the beginning time of the wakeword;
matching the sequence of words to a complete command;
identifying a mid-sentence correction between the command and the wakeword, the mid-sentence correction corresponding to words matching a second complete command; and;
invoking a function associated with the second complete command.
14. The computer-implemented method of claim 13 , wherein using R-ASR further comprises referencing at least a phonetic dictionary, the phonetic dictionary having reverse pronunciations of words.
15. The computer-implemented method of claim 13 , wherein using R-ASR further comprises referencing at least a language model, the language model having reverse orders of word sequences.
16. The computer-implemented method of claim 13 , further comprising:
transforming the audio signal into a sequence of reversed phonemes through R-ASR.
17. The computer-implemented method of claim 13 , further comprising:
terminating R-ASR when the sequence of words matches the complete command.
18. The computer-implemented method of claim 13 , further comprising:
estimating a last frame of the wakeword;
converting a second plurality of frames into a second sequence of words using automatic speech recognition (ASR) system, in oldest to newest order, beginning from a first frame after the last frame of the wakeword;
combining the sequence of words and the second sequence of words into a combined sequence of words;
matching the combined sequence of words to the complete command; and
invoking the function associated with the complete command.
19. The computer-implemented method of claim 13 , wherein using ASR further comprises referencing at least a second phonetic dictionary, the second phonetic dictionary having forward pronunciations of words.
20. The computer-implemented method of claim 13 , wherein using ASR further comprises referencing at least a second language model, the second language model having forward orders of word sequences.