Effects of compound parameters on speaker-independent word recognition.

Text independent speaker recognition using speaker dependent word spotting

This paper is motivated by the fact that text dependent speaker recognition is inherently more accurate than text independent speaker recognition. In this work we assign models to frequent words spoken by a speaker and spot them in a test call. In this way, text-dependent speaker recognition technology can be used for text independent tasks. The approach we take is to use DTW (Dynamic Time Warp...

Filtering the time sequence of spectral parameters for speaker-independent CDHMM word recognition

In this work, we show how speaker-independent CDHMM word recognition performance can be significantly improved for clean speech by filtering the time sequence of spectral parameters to enhance its time dynamics. Experimental results with the standard TI connected digits database show the filter can achieve more than 30% reduction of string recognition error. As shown in this paper, that improve...

The design of acoustic parameters for speaker-independent speech recognition

This paper presents a two-stage procedure, based on the Fisher criterion and automatic classi cation trees, for designing acoustic parameters (APs) that target phonetic features in the speech signal. This procedure and a subset of the TIMIT training set were used to develop acoustic parameters for the phonetic features: sonorant, syllabic, strident, palatal, alveolar, labial and velar. Results ...

Using condensed nearest neighbor rule for speaker independent word recognition

Minimizing Speaker Variation Effects for Speaker-Independent Speech Recognition

1. I N T R O D U C T I O N For speaker-independent speech recognition, speaker variation is one of the major error sources. As a typical example, the error rate of a well-trained speaker-dependent speech recognition system is three times less than that of a speaker-independent speech recognition system [11]. To minimize speaker variation effects, we can use either speakerclustered models [28, 1...