Apparatus and method for speech recognition in the presence of unnatural speech effects

A speech recognition apparatus comprising:

• an acoustic analyzer for analyzing an input speech signal of unnatural speech and extracting a time-series feature vector from said input speech signal;
• a normal speech model memory for storing a normal speech model learned based on normal speech data;
• an acoustic-phonetic variability model memory for storing an acoustic-phonetic variability model representing an acoustic-phonetic change of spectrum caused by said unnatural speech;
• and
• speech learning means for learning said acoustic-phonetic change with said time-series feature vector based on said normal speech model and for generating said acoustic-phonetic variability model.

A speech recognition apparatus, comprising:

• an acoustic analyzer for analyzing an input speech signal of unnatural speech and extracting a time-series feature vector from said input speech signal;
• a normal speech model memory for storing a normal speech model learned based on normal speech data;
• speech learning means for learning an acoustic-phonetic change based on said time-series feature vector and said normal speech model and for generating an acoustic-phonetic variability model based upon said acoustic-phonetic change;
• duration change learning means for learning a duration change by unnatural speech on a phonological unit basis based on said acoustic-phonetic variability model and said normal speech model, and for generating duration change data based upon said duration change;
• and
• a duration memory for storing said duration change data.

A speech recognition method, comprising the steps of:

• storing a plurality of normal speech models, a plurality of acoustic-phonetic variability models corresponding to some of said plurality of normal speech models, and a plurality of values of weight coefficients, each representing a similarity of one of said plurality of normal speech models to another of said plurality of normal speech models;
• selecting a selected plurality of said acoustic-phonetic variability models having highest values of said weight coefficient with one of said normal speech models, modifying a spectrum of said one of normal speech models based on each one of said selected plurality of acoustic-phonetic variability models, and generating a plurality of modified spectra of said one of said normal speech models;
• calculating a mean value of said plurality of modified spectra to generate a modified normal speech model based on said mean value;
• and
• comparing said mean value modified normal speech model with input unnatural speech data and outputting a comparison result.

A speech recognition method, comprising the steps of:

• analyzing an input speech signal to extract a time-series feature vector from said input speech signal;
• learning normal speech data, generating a normal speech model including a duration parameter, and storing said normal speech model;
• learning an acoustic-phonetic variability model representing an acoustic-phonetic change of spectrum caused by unnatural speech based on said normal speech model and said time-series feature vector;
• calculating a duration change by unnatural speech on a phonological unit basis based on said normal speech model and said acoustic-phonetic variability model, and storing duration change data;
• modifying said duration parameter of said normal speech model based on said duration change data, and generating a parameter-modified normal speech model;
• and
• recognizing said time-series feature vector based on said parameter-modified normal speech model and said acoustic-phonetic variability model, and outputting a recognition result.

A speech recognition apparatus for recognizing an input utterance having an acoustic-phonetic change of spectrum caused by unnatural speech, said speech recognition apparatus comprising:

• an acoustic analyzer for extracting a feature vector from said input utterance;
• a normal speech data memory for providing a learning result of normal speech data;
• a memory for providing a learning result of said acoustic-phonetic change;
• and
• a speech recognition unit having an input that receives said feature vector and an output that provides a recognition result, said speech recognition unit comprising:
• means for modifying said learning result off normal speech data based on at least one of said learning results of acoustic-phonetic change and for generating a modified speech model based on said acoustic-phonetic change, said at least one of said learning results of acoustic-phonetic change may not correspond to said learning result of normal speech model.

A speech recognition apparatus comprising:

• an acoustic analyzer for extracting a feature vector from an input utterance;
• a normal speech data memory for providing a learning result of normal speech data;
• and
• a speech learning unit including:
• means for learning an acoustic-phonetic change of spectrum caused by unnatural speech to generate a learning result;
• and
• means for modifying said learning result of normal speech data based on at least one of said learning result of said acoustic-phonetic change to generate a reference speech model.