Isolated word training and detection

Patent metadata

Field	Value
Publication number	US-2016189706-A1
Application number	US-201514606588-A
Country	US
Kind code	A1
Filing date	Jan 27, 2015
Priority date	Dec 30, 2014
Publication date	Jun 30, 2016
Grant date	—

Abstract

Methods, systems, and apparatuses are described for isolated word training and detection. Isolated word training devices and systems are provided in which a user may provide a wake-up phrase from 1 to 3 times to train the device or system. A concatenated phoneme model of the user-provided wake-up phrase may be generated based on the provided wake-up phrase and a pre-trained phoneme model database. A word model of the wake-up phrase may be subsequently generated from the concatenated phoneme model and the provided wake-up phrase. Once trained, the user-provided wake-up phrase may be used to unlock the device or system and/or to wake up the device or system from a standby mode of operation. The word model of the user-provided wake-up phrase may be further adapted based on additional provisioning of the wake-up phrase.

First claim

What is claimed is:

1 . An isolated word training system that comprises:
an input component configured to receive at least one audio input representation; a recognition component configured to generate a phoneme concatenation model of the at least one audio input representation based on a phoneme transcription; an adaptation component configured to generate a first word model of the at least one audio input based on the phoneme concatenation model.

2 . The isolated word training system of claim 1 , wherein the adaptation component is further configured to generate a second word model based on the first word model and at least one additional audio input representation.

3 . The isolated word training system of claim 1 , that further comprises:
a phoneme model database that includes the one or more stored phoneme models, each of the one or more stored phoneme models being a pre-trained Hidden Markov Model.

4 . The isolated word training system of claim 1 , wherein the recognition component is configured to generate the phoneme concatenation model by:
decoding feature vectors of the at least one audio input using the one or more stored phoneme models to generate the phoneme transcription that comprises one or more phoneme identifiers; selecting a subset of phoneme identifiers from the phoneme transcription; and selecting corresponding phoneme models from a phoneme model database for each phoneme identifier in the subset as the phoneme concatenation model.

5 . The isolated word training system of claim 4 , that further comprises:
a feature extraction component configured to:
derive speech features of speech of the at least one audio input; and
generate the feature vectors based on the speech features.

6 . The isolated word training system of claim 5 , that further comprises:
a voice activity detection component configured to:
detect an onset of the speech;
determine a termination of the speech; and
provide the speech to the feature extraction component.

7 . The isolated word training system of claim 1 , wherein the adaptation component is configured to generate the first word model by at least one of:
removing one or more phonemes from the phoneme concatenation model; combining one or more phonemes of the phoneme concatenation model; adapting one or more state transition probabilities of the phoneme concatenation model; adapting an observation symbol probability distribution of the phoneme concatenation model; or adapting the phoneme concatenation model as a whole.

8 . An electronic device that comprises:
a first processing component configured to:
receive a user-specified wake-up phrase,
generate a phoneme concatenation model of the user-specified wake-up phrase, and
generate a word model of the user-specified wake-up phrase based on the phoneme concatenation model; and
a second processing component configured to:
detect audio activity of the user, and
determine if the user-specified wake-up phrase is present within the audio activity based on the word model.

9 . The electronic device of claim 8 , wherein the first processing component is further configured to operate in a training mode of the electronic device, and
wherein the second processing component is further configured to:
operate in a stand-by mode of the electronic device, and
provide, in the stand-by mode, an indication to the electronic device to operate in a normal operating mode subsequent to a determination that the user-specified wake-up phrase is present in the audio activity.

10 . The electronic device of claim 9 , that further comprises:
a memory component configured to:
buffer the audio activity and the user-specified wake-up phrase of the user; and
provide the audio activity or the user-specified wake-up phrase to at least one of a voice activity detector or an automatic speech recognition component.

11 . The electronic device of claim 9 , wherein the first processing component, in the training mode, is further configured to:
decode feature vectors of the at least one audio input using the one or more stored phoneme models to generate a phoneme transcription that comprises one or more phoneme identifiers; select a subset of phoneme identifiers from the phoneme transcription; and select corresponding phoneme models from a phoneme model database for each phoneme identifier in the subset as the phoneme concatenation model.

12 . The electronic device of claim 11 , wherein the first processing component, in the training mode, is further configured to:
derive speech features of the user-specified wake-up phrase; and generate the feature vectors based on the speech features.

13 . The electronic device of claim 9 , wherein the second processing component, in the stand-by mode, is further configured to:
derive speech features of the audio activity; generate feature vectors based on the speech features; and determine if the user-specified wake-up phrase is present by comparing the feature vectors to the word model.

14 . The electronic device of claim 8 , wherein the first processing component is further configured to:
generate the first word model by at least one of:
removing one or more phonemes from the phoneme concatenation model;
combining one or more phonemes of the phoneme concatenation model;
adapting one or more state transition probabilities of the phoneme concatenation model;
adapting an observation symbol probability distribution of the phoneme concatenation model; or
adapting the phoneme concatenation model as a whole.

15 . The electronic device of claim 8 , wherein the first processing component is further configured to update the word model based on the user-specified wake-up phrase in a subsequent audio input.

16 . The electronic device of claim 8 , that further comprises:
a phoneme model database that includes one or more stored phoneme models, each of the one or more stored phoneme models being a pre-trained Hidden Markov Model.

17 . A computer-readable storage medium having program instructions recorded thereon that, when executed by an electronic device, perform a method for utilizing a user-specified wake-up phrase in the electronic device, the method comprising:
training the electronic device using the user-specified wake-up phrase, received from a user, to generate, at the electronic device, a user-dependent word model of the user-specified wake-up phrase; and transitioning the electronic device from a stand-by mode to a normal operating mode subsequent to a detection, that is based on the word model, of the user-specified wake-up phrase.

18 . The computer-readable storage medium of claim 17 , wherein training the electronic device using the user-specified wake-up phrase includes receiving the user-specified wake-up phrase from the user no more than three times.

19 . The computer-readable storage medium of claim 18 , wherein the electronic device generates the word model based on a phoneme concatenation model of the user-specified wake-up phrase.

20 . The computer-readable storage medium of claim 19 , wherein the training further comprises:
generating a phoneme transcription of the user-specified wake-up phrase that includes one or more phoneme identifiers; and generating the phoneme concatenation model of the user-specified wake-up phrase based on stored phoneme models that correspond to the one or more phoneme identifiers.

Assignees

• Broadcom Corp

Inventors

• Zopf Robert W
• Borgstrom Bengt J

Classifications

• G10L15/063 Primary

  Training · CPC title

• G10L15/02 Primary

  Feature extraction for speech recognition; Selection of recognition unit · CPC title

• G10L2015/025

  Phonemes, fenemes or fenones being the recognition units · CPC title

• G10L15/05

  Word boundary detection · CPC title

• G10L15/144

  Training of HMMs · CPC title