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-independent voiced-stop-consonant recognition using a block-windowed neural network architecture

Abstract: The authors study several of the more well-known connectionist models, and how they address the time and frequency variability of the multispeaker, voiced-stop-consonant recognition task. Among the network architectures reviewed or tested for were the self-organizing feature maps (SOFM) architecture, various derivatives of this architecture, the time-delay neural network (TDNN) architecture, various derivatives of this architecture, and two frequency-and-time-shift-invariant architectures, frequency-shift-invariant TDNN, and the block-windowed neural network (FTDNN and BWNN). Voiced-stop speech was extracted from up to four dialect regions of the TIMIT continuous speech corpus for subsequent preprocessing and training and testing of network instances. Various feature representations were tested for their robustness in representing the voiced-stop consonants.

Dynamic evidence models in a DBN phone recognizer.

Abstract: This paper describes an implementation of a discriminative acoustical model – a Conditional Random Field (CRF) – within a Dynamic Bayes Net (DBN) formulation of a Hierarchic Hidden Markov Model (HHMM) phone recognizer. This CRF-DBN topology accounts for phone transition dynamics in conditional probability distributions over random variables associated with observed evidence, and therefore has less need for hidden variable states corresponding to transitions between phones, leaving more hypothesis space available for modeling higher-level linguistic phenomena such syntax and semantics. The model also has the interesting property that it explicitly represents likely formant trajectories and formant targets of modeled phones in its random variable distributions, making it more linguistically transparent than models based on traditional HMMs with conditionally independent evidence variables. Results on the standard TIMIT phone recognition task show this CRF evidence model, even with a relatively simple first-order feature set, is competitive with standard HMMs and DBN variants using static Gaussian mixture models on MFCC features.

A simple perceptual method for quantizing wavelet packet coefficients of wideband speech

Abstract: In this paper an efficient and low complexity perceptual method is proposed for quantizing the wavelet packet coefficients of high quality speech signals. The performance of the proposed method is compared, using the same codec, with the case where all coefficients are quantized using a fixed number of bits. The results on 500 TIMIT files show that this method based on some basic perceptual considerations achieves about 15–35% reduction in the average bit-rates with almost the same or even better perceptual qualities.

Syllable nucleus Durations Estimation using Linear Regression based ensemble model

Abstract: Unlike conventional automatic continuous speech segmentation models that deal with each boundary time-mark individually, in this paper, we propose an interval-data-based Linear Regression Model for syllable nucleus Durations Estimation (LRM-DE), which treats syllable boundary time-marks in pairs. This characteristic of LRM-DE makes it more suitable for estimating syllable durations for English sentences, which can be used for sentence stress detection. LRM-DE combines the outcomes of multiple base automatic speech segmentation machines (ASMs) to generate final boundary time-marks that miminize the average distance of the predicted and reference boundary-pairs of syllable nuclei. Experimental results show that on TIMIT dataset, LRM-DE reduces the average difference between the predicted syllable nucleus durations and their reference ones from 13.64ms (the best result of a single ASM) to 11.81ms. Also, LRM-DE improves the syllable nucleus segmentation accuracy from 81.59% to 83.98% within a tolerance of 20ms.