TIMIT

About: TIMIT is a research topic. Over the lifetime, 1401 publications have been published within this topic receiving 59888 citations. The topic is also known as: TIMIT Acoustic-Phonetic Continuous Speech Corpus.

Speaker Recognition using Random Forest

Abstract: Speaker identification has become a mainstream technology in the field of machine learning that involves determining the identity of a speaker from his/her speech sample. A person’s speech note contains many features that can be used to discriminate his/her identity. A model that can identify a speaker has wide applications such as biometric authentication, security, forensics and human-machine interaction. This paper implements a speaker identification system based on Random Forest as a classifier to identify the various speakers using MFCC and RPS as feature extraction techniques. The output obtained from the Random Forest classifier shows promising result. It is observed that the accuracy level is significantly higher in MFCC as compared to the RPS technique on the data taken from the well-known TIMIT corpus dataset.

Largemargin training of semi-Markov model for phonetic recognition

Abstract: This paper considers a large margin training of semi-Markov model (SMM) for phonetic recognition. The SMM framework is better suited for phonetic recognition than the hidden Markov model (HMM) framework in that the SMM framework is capable of simultaneously segmenting the uttered speech into phones and labeling the segment-based features. In this paper, the SMM framework is used to define a discriminant function that is linear in the joint feature map which attempts to capture the long-range statistical dependencies within a segment and between adjacent segments of variable length. The parameters of the discriminant function are estimated by a large margin learning criterion for structured prediction. The parameter estimation problem, which is an optimization problem with many margin constraints, is solved by using a stochastic subgradient descent algorithm. The proposed large margin SMM outperforms the large margin HMM on the TIMIT corpus.

A prediction method using instantaneous mixing plus auto regressive approach in frequency domain for separating speech signals by short time fourier transform

Abstract: The revealed works of separation of speech signals, the most disadvantages is that the incidences of distortion speech at intervals the signal that affects separated signal with loud musical noise The thought for speech separation in normal Blind Source Separation (BSS) ways in which is solely one sound supply in an exceedingly single area The projected methodology uses as a network that has the parameters of the Instantaneous Mixing Auto Regressive model (IMAR) for the separation matrices over the entire frequency vary A trial has been created to estimate the simplest values of the Instantaneous Mixing Auto Regressive model (IMAR) model parameters using two matrices W and G by suggests that of the maximum-likelihood estimation methodology Supported the values of those parameters, the supply spectral half vectors square measure calculable The whole set of Texas Instruments Massachusetts Institute of Technology (TIMIT) corpus is utilized for speech materials in evolution results The Signal to Interference quantitative relation (SIR) improves by a median of 5dB unit of measurement over a frequency domain BSS approach

Joint optimization on decoding graphs using minimum classification error criterion

Abstract: Motivated by the inherent correlation between the speech features and their lexical words, we propose in this paper a new framework for learning the parameters of the corresponding acoustic and language models jointly. The proposed framework is based on discriminative training of the models' parameters using minimum classification error criterion. To verify the effectiveness of the proposed framework, a set of four large decoding graphs is constructed using weighted finite-state transducers as a composition of two sets of context-dependent acoustic models and two sets of n-gram-based language models. The experimental results conducted on this set of decoding graphs validated the effectiveness of the proposed framework when compared with four baseline systems based on maximum likelihood estimation and separate discriminative training of acoustic and language models in benchmark testing of two speech corpora, namely TIMIT and RM1.

Improving deep neural networks using state projection vectors of subspace Gaussian mixture model as features

Abstract: Recent advancement in deep neural network (DNN) has surpassed the conventional hidden Markov model-Gaussian mixture model (HMM-GMM) framework due to its efficient training procedure. Providing better phonetic context information in the input gives improved performance for DNN. The state projection vectors (state specific vectors) in subspace Gaussian mixture model (SGMM) captures the phonetic information in low dimensional vector space. In this paper, we propose to use state specific vectors of SGMM as features thereby providing additional phonetic information for the DNN framework. To each observation vector in the train data, the corresponding state specific vectors of SGMM are aligned to form the state specific vector feature set. Linear discriminant analysis (LDA) feature set are formed by applying LDA to the training data. Since bottleneck features are efficient in extracting useful discriminative information for the phonemes, LDA feature set and state specific vector feature set are converted to bottleneck features. These bottleneck features of both feature sets act as input features to train a single DNN framework. Relative improvement of 8.8% for TIMIT database (core test set) and 9.7% for WSJ corpus is obtained by using the state specific vector bottleneck feature set when compared to the DNN trained only with LDA bottleneck feature set. Also training Deep belief network - DNN (DBN-DNN) using the proposed feature set attains a WER of 20.46% on TIMIT core test set proving the effectiveness of our method. The state specific vectors while acting as features, provide additional useful information related to phoneme variation. Thus by combining it with LDA bottleneck features improved performance is obtained using the DNN framework.