SPEECH RECOGNITION APPARATUS BASED ON CEPSTRUM FEATURE VECTOR AND METHOD THEREOF

• US 20130138437A1
• Filed: 07/25/2012
• Published: 05/30/2013
• Est. Priority Date: 11/24/2011
• Status: Abandoned Application

• Current Assignee: Electronics and Telecommunications Research Institute
• Original Assignee: Electronics and Telecommunications Research Institute
• Inventors: Cho, Hoon-Young, Kim, Sanghun, Kim, Youngik
• Application Number:
  US13/558,236
• Publication Number:
  US 20130138437A1
• Time in Patent Office: Days
• US Class Current: 704/233
• CPC Class Codes: G10L 15/20 Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech G10L21/02 takes precedence

Abstract

A speech recognition apparatus, includes a reliability estimating unit configured to estimate reliability of a time-frequency segment from an input voice signal; and a reliability reflecting unit configured to reflect the reliability of the time-frequency segment to a normalized cepstrum feature vector extracted from the input speech signal and a cepstrum average vector included for each state of an HMM in decoding. Further, the speech recognition apparatus includes a cepstrum transforming unit configured to transform the cepstrum feature vector and the average vector through a discrete cosine transformation matrix and calculate a transformed cepstrum vector. Furthermore, the speech recognition apparatus includes an output probability calculating unit configured to calculate an output probability value of time-frequency segments of the input speech signal by applying the transformed cepstrum vector to the cepstrum feature vector and the average vector.

Claims

1. A speech recognition apparatus based on a cepstrum feature vector, comprising:

a reliability estimating unit configured to estimate reliability of a time-frequency segment from an input voice signal;

a reliability reflecting unit configured to reflect the reliability of the time-frequency segment to a normalized cepstrum feature vector extracted from the input speech signal and a cepstrum average vector included for each state of an HMM (Hidden Marcov Model) in decoding;

a cepstrum transforming unit configured to transform the cepstrum feature vector and the average vector in which the reliability is reflected, through a discrete cosine transformation matrix and calculate a transformed cepstrum vector; and

an output probability calculating unit configured to calculate an output probability value of time-frequency segments of the input speech signal by applying the transformed cepstrum vector to the cepstrum feature vector and the average vector in which the reliability is reflected.

2. The speech recognition apparatus of claim 1, wherein the reliability estimating unit estimates a reliability value between 0 and 1 for q frequency sub-bands at each frame of the input speech signal and stores the reliability value in the type of Q-order reliability vector at each frame.

3. The speech recognition apparatus of claim 2, wherein the reliability reflecting unit reflects reliability of a time-frequency segment at each frame.

4. The speech recognition apparatus of claim 2, wherein the reliability reflecting unit transforms the cepstrum feature vector of the input speech signal and the average vector of the HMM into a log spectrum vector space by applying an inverse discrete cosine transformation matrix, multiplies by the reliability matrix of the time-frequency segment, and then transforms the cepstrum feature vector and the average vector into a cepstrum vector space by applying a discrete cosine transformation matrix.

5. The speech recognition apparatus of claim 1, wherein the output probability calculating unit applies the transformed cepstrum vector to the average vector of the HMM and the input speech signal such that time-frequency segments with relatively low reliability are relatively less reflected to the output probability value when the output probability value is calculated.

6. The speech recognition apparatus of claim 1, wherein the reliability reflecting unit also processes the normalized time-frequency segment such that the average vector value of the overall feature vector rows of the input speech signal becomes 0, when reflecting the cepstrum vector to the input voice signal.

7. A speech recognition method based on a cepstrum feature vector, comprising:

estimating reliability of a time-frequency segment from an input voice signal;

normalizing a cepstrum feature vector extracted from the input voice signal;

reflecting the reliability of the time-frequency segment to a cepstrum average vector included for each state of an HMM in decoding of the input voice signal;

transforming the cepstrum feature vector and the average vector where the reliability is reflected, through a discrete cosine transformation matrix and calculating a transformed cepstrum vector; and

calculating an output probability value of time-frequency segments of the input speech signal by applying the transformed cepstrum vector to the cepstrum feature vector and the average vector in which the reliability is reflected.

8. The speech recognition method of claim 7, wherein said estimating reliability is performed such that a reliability value between 0 and 1 is estimated for q frequency sub-bands at each frame of the input speech signal and the reliability value is stored in the type of Q-order reliability vector at each frame.

9. The speech recognition method of claim 7, wherein said reflecting reliability includes:

transforming the cepstrum feature vector of the input speech signal and the average vector of the HMM into a log spectrum vector space by applying an inverse discrete cosine transformation matrix; and

transforming the cepstrum feature vector and the average vector into a cepstrum vector space by applying a discrete cosine transformation matrix after multiplying by the reliability matrix of the time-frequency segment.

10. The speech recognition method of claim 7, wherein said reflecting reliability is performed such that reliability of a time-frequency segment is reflected at each frame.

11. The speech recognition method of claim 7, wherein said calculating output probability is performed such that the transformed cepstrum vector is applied to the average vector of the HMM and the input speech signal such that time-frequency segments with relatively low reliability are relatively less reflected to the output probability value when the output probability value is calculated.

12. The speech recognition method of claim 7, wherein said reflecting reliability is performed such that the normalized time-frequency segment is also processed such that the average vector value of the overall feature vector rows of the input speech signal becomes 0, when the cepstrum vector to the input speech signal is reflected.