Topic
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.
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TL;DR: An approach based on noise eigenspace projections to pack the color component into a subspace, named “noise subspace”, which efficiently reduces noise with little speech distortion.
Abstract: How to reduce noise with less speech distortion is a challenging issue for speech enhancement. We propose a novel approach for reducing noise with the cost of less speech distortion. A noise signal can generally be considered to consist of two components, a “white-like” component with a uniform energy distribution and a “color” component with a concentrated energy distribution in some frequency bands. An approach based on noise eigenspace projections is proposed to pack the color component into a subspace, named “noise subspace”. This subspace is then removed from the eigenspace to reduce the color component. For the white-like component, a conventional enhancement algorithm is adopted as a complementary processor. We tested our algorithm on a speech enhancement task using speech data from the Texas Instruments and Massachusetts Institute of Technology (TIMIT) dataset and noise data from NOISEX-92. The experimental results show that the proposed algorithm efficiently reduces noise with little speech distortion. Objective and subjective evaluations confirmed that the proposed algorithm outperformed conventional enhancement algorithms.
01 Jan 2007
TL;DR: This paper shows that frame selection behavior is phoneme dependent, and observes that some phonemes benefit from frame selection while others do not, and that this separation matches the phonetic categories.
Abstract: In previous study we proposed algorithms to select representative frames from a segment for phoneme likelihood evaluation. In this paper we show that this frame selection behavior is phoneme dependent. We observe that some phonemes benefit from frame selection while others do not, and that this separation matches the phonetic categories. For those phonemes sensitive to frame selection, we find that selecting frames at some pre-defined positions in the segment enhances the discrimination between phonemes. These phoneme-dependent positions are explicitly retrieved and used in a phoneme classification task. Experimental results on the TIMIT phonetic database show that the frame selection method significantly outperforms decoding by the classical Viterbi decoder.
30 Sep 1993
TL;DR: There is a need for lower-data-rate voice encoders for special applications: improved performance in high bit-error conditions, low- probability-of-intercept (LPI) voice communication, and narrowband integrated voice/data systems.
Abstract: : The 2400-b/s linear predictive coder (LPC) is currently being widely deployed to support tactical voice communication over narrowband channels. However, there is a need for lower-data-rate voice encoders for special applications: improved performance in high bit-error conditions, low- probability-of-intercept (LPI) voice communication, and narrowband integrated voice/data systems. An 800-b/s voice encoding algorithm is presented which is an extension of the 2400-b/s LPC. To construct template tables, speech samples of 420 speakers uttering 8 sentences each were excerpted from the Texas Instrument - Massachusetts Institute of Technology (TIMIT) Acoustic-Phonetic Speech Data Base. Speech intelligibility of the 800-b/s voice encoding algorithm measured by the diagnostic rhyme test (DRT) is 91.5 for three male speakers. This score compares favorably with the 2400-b/s LPC of a few years ago.
TL;DR: In this paper , a neural network that evaluates an ideal binary mask IBM using features extracted from a mixture of near-end and far-end signals was used to solve the problem of acoustic echo suppression.
Abstract: The article solves the problem of acoustic echo suppression based on a neural network that evaluates an ideal binary mask IBM using features extracted from a mixture of near-end and far-end signals. The novelty of the proposed method lies in the use of the clustering algorithm in addition to the bidirectional recurrent neural network BLSTM. To evaluate the use of the EM, Mean-Shift, k-Means clustering algorithms, the models have been trained and tested on the TIMIT database. For each model, the ERLE, PESQ, STOI metrics have been calculated to characterize its quality. The use of the EM and Mean-Shift clustering algorithms appeared to be inefficient compared to the BLSTM algorithm at a signal-to-echo ratio of 10 dB. With a signal-to-echo ratio of 6 dB, BLSTM+Mean-Shift resulted in a marginal improvement in the PESQ metric compared to the BLSTM algorithm. The results of the experiments show the effectiveness of the proposed BLSTM model when using a network with the K-Means algorithm, compared to using a pure BLSTM for echo cancellation in double-talk scenarios. With a signal-to-echo ratio of 10 dB, the STOI metric, which characterizes speech intelligibility, has improved by 7%, and the PESQ metric, which characterizes the quality of speech restoration, by 18.8%.