Papers by K. Ramakrishnan
This paper describes the development of an efficient speech recognition system using different
te... more This paper describes the development of an efficient speech recognition system using different
techniques such as Mel Frequency Cepstrum Coefficients (MFCC), Vector Quantization (VQ) and
Hidden Markov Model (HMM).
This paper explains how speaker recognition followed by speech recognition is used to recognize the
speech faster, efficiently and accurately. MFCC is used to extract the characteristics from the input
speech signal with respect to a particular word uttered by a particular speaker. Then HMM is used
on Quantized feature vectors to identify the word by evaluating the maximum log likelihood values
for the spoken word.
Uploads
Papers by K. Ramakrishnan
techniques such as Mel Frequency Cepstrum Coefficients (MFCC), Vector Quantization (VQ) and
Hidden Markov Model (HMM).
This paper explains how speaker recognition followed by speech recognition is used to recognize the
speech faster, efficiently and accurately. MFCC is used to extract the characteristics from the input
speech signal with respect to a particular word uttered by a particular speaker. Then HMM is used
on Quantized feature vectors to identify the word by evaluating the maximum log likelihood values
for the spoken word.
techniques such as Mel Frequency Cepstrum Coefficients (MFCC), Vector Quantization (VQ) and
Hidden Markov Model (HMM).
This paper explains how speaker recognition followed by speech recognition is used to recognize the
speech faster, efficiently and accurately. MFCC is used to extract the characteristics from the input
speech signal with respect to a particular word uttered by a particular speaker. Then HMM is used
on Quantized feature vectors to identify the word by evaluating the maximum log likelihood values
for the spoken word.