Speaker-dependent isolated word recognition using speaker-independent vector quantization codebooks augmented with speaker-specific data

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...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Speaker Independent Kannada Speech Recognition using Vector quantization

In this paper a statistical method is used to remove the silence from the speech signal. This method is applied on vector quantization technique to identify the minimum speech patterns that are required while creating the training set of the speech samples. This paper also discusses the importance and efficiency of the algorithms used in vector quantization for the clustering purpose. Also spee...

Automatic Speaker Recognition Using Fuzzy Vector Quantization

Speaker recognition (SR) is a dynamic biometric task. SR is a multidisplinary problem that encompasses many aspects of human speech, including speech recognition, language recognition, and speech accents. This technique makes it possible to use the speaker’s voice to verify his/her identity and provide controlled access to services. The Mel-frequency extraction method is leading approach for sp...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words