Mel-frequency cepstral coefficients (MFCC) are the most widely used features in current speech recognition systems. However, they have a poor physical interpretation and they do not lie in the frequency domain. Frequency filtering (FF) is a technique that has been recently developed to design frequency-localized speech features that perform similar to MFCC in terms of recognition performances. ...

A number of intricate deep learning architectures for effective End-to-End (E2E) speech recognition systems have emerged due to recent advancements in algorithms and technical resources. The proposed work develops an ASR system the publicly accessible dataset on Gujarati language. approach provided this research combines features like Mel frequency Cepstral Coefficients (MFCC) Constant Q (CQCC)...

Abstract Many people are interested in instrumental music. They may have one piece of song, but it is a challenge to seek the song because they do not lyrics describe for text-based search engine. This study leverages Approximate Nearest Neighbours preprocess songs and extract characteristics track repository using Mel frequency cepstral coefficients (MFCC) characteristic extraction. Our method...

We explore a new spectral representation of speech signals through group delay functions. The group delay functions by themselves are noisy and difficult to interpret owing to zeroes that are close to the unit circle in the z-domain and these clutter the spectra. A new modified group delay function [1] that reduces the effects of zeroes close to the unit circle is used. Assuming that this new f...

Speaking is the most basic and efficient mode of human contact. Emotions assist people in communicating understanding others’ viewpoints by transmitting sentiments providing feedback.The objective speech emotion recognition to enable computers comprehend emotional states such as happiness, fury, disdain through voice cues. Extensive Effective Method Coefficients Mel cepstral frequency have been...

The paper discusses the use, in a hybrid recognizer, of gravity centers (gc) in spectral subbands as features to be used in addition to Mel Scaled Cepstral Coefficients (MFCC) and their time derivatives. Results on noisy telephone speech show that gc computed after the nonlinear processing of an ear model increase the word accuracy from 72.63% to 78.13% .

In this paper two popular feature extraction techniques Linear Predictive Cepstral Coefficients (LPCC) and Mel Frequency Cepstral Coefficients (MFCC) have been investigated and their performances have been evaluated for the recognition of Assamese phonemes. A multilayer perceptron based baseline phoneme recognizer has been built and all the experiments have been carried out using that recognize...

This thesis is concerned with reconstructing an intelligible time-domain speech signal from speech recognition features, such as Mel-frequency cepstral coefficients (MFCCs), in a distributed speech recognition(DSR) environment. The initial reconstruction methods in this thesis require, in addition to MFCC vectors, fundamental frequency and voicing information. In the later parts of the thesis t...

This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are generally considered unusable for speech synthesis. First, we predict fundamental frequency and voicing information from MFCCs with an autoregressive recurrent neural net. Second, the spectral envelope information conta...