535 research outputs found
Deep Long Short-Term Memory Adaptive Beamforming Networks For Multichannel Robust Speech Recognition
Far-field speech recognition in noisy and reverberant conditions remains a
challenging problem despite recent deep learning breakthroughs. This problem is
commonly addressed by acquiring a speech signal from multiple microphones and
performing beamforming over them. In this paper, we propose to use a recurrent
neural network with long short-term memory (LSTM) architecture to adaptively
estimate real-time beamforming filter coefficients to cope with non-stationary
environmental noise and dynamic nature of source and microphones positions
which results in a set of timevarying room impulse responses. The LSTM adaptive
beamformer is jointly trained with a deep LSTM acoustic model to predict senone
labels. Further, we use hidden units in the deep LSTM acoustic model to assist
in predicting the beamforming filter coefficients. The proposed system achieves
7.97% absolute gain over baseline systems with no beamforming on CHiME-3 real
evaluation set.Comment: in 2017 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP
Improved Noisy Student Training for Automatic Speech Recognition
Recently, a semi-supervised learning method known as "noisy student training"
has been shown to improve image classification performance of deep networks
significantly. Noisy student training is an iterative self-training method that
leverages augmentation to improve network performance. In this work, we adapt
and improve noisy student training for automatic speech recognition, employing
(adaptive) SpecAugment as the augmentation method. We find effective methods to
filter, balance and augment the data generated in between self-training
iterations. By doing so, we are able to obtain word error rates (WERs)
4.2%/8.6% on the clean/noisy LibriSpeech test sets by only using the clean 100h
subset of LibriSpeech as the supervised set and the rest (860h) as the
unlabeled set. Furthermore, we are able to achieve WERs 1.7%/3.4% on the
clean/noisy LibriSpeech test sets by using the unlab-60k subset of LibriLight
as the unlabeled set for LibriSpeech 960h. We are thus able to improve upon the
previous state-of-the-art clean/noisy test WERs achieved on LibriSpeech 100h
(4.74%/12.20%) and LibriSpeech (1.9%/4.1%).Comment: 5 pages, 5 figures, 4 tables; v2: minor revisions, reference adde
Bidirectional truncated recurrent neural networks for efficient speech denoising
We propose a bidirectional truncated recurrent neural network architecture for speech denoising. Recent work showed that deep recurrent neural networks perform well at speech denoising tasks and outperform feed forward architectures [1]. However, recurrent neural networks are difficult to train and their simulation does not allow for much parallelization. Given the increasing availability of parallel computing architectures like GPUs this is disadvantageous. The architecture we propose aims to retain the positive properties of recurrent neural networks and deep learning while remaining highly parallelizable. Unlike a standard recurrent neural network, it processes information from both past and future time steps. We evaluate two variants of this architecture on the Aurora2 task for robust ASR where they show promising results. The models outperform the ETSI2 advanced front end and the SPLICE algorithm under matching noise conditions.We propose a bidirectional truncated recurrent neural network architecture for speech denoising. Recent work showed that deep recurrent neural networks perform well at speech denoising tasks and outperform feed forward architectures [1]. However, recurrent neural networks are difficult to train and their simulation does not allow for much parallelization. Given the increasing availability of parallel computing architectures like GPUs this is disadvantageous. The architecture we propose aims to retain the positive properties of recurrent neural networks and deep learning while remaining highly parallelizable. Unlike a standard recurrent neural network, it processes information from both past and future time steps. We evaluate two variants of this architecture on the Aurora2 task for robust ASR where they show promising results. The models outperform the ETSI2 advanced front end and the SPLICE algorithm under matching noise conditions.P
Comparative Analysis of MLP, CNN, and RNN Models in Automatic Speech Recognition: Dissecting Performance Metric
This study conducts a comparative analysis of three prominent machine learning models: Multi-Layer Perceptrons (MLP), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) in the field of automatic speech recognition (ASR). This research is distinct in its use of the LibriSpeech 'test-clean' dataset, selected for its diversity in speaker accents and varied recording conditions, establishing it as a robust benchmark for ASR performance evaluation. Our approach involved preprocessing the audio data to ensure consistency and extracting Mel-Frequency Cepstral Coefficients (MFCCs) as the primary features, crucial for capturing the nuances of human speech. The models were meticulously configured with specific architectural details and hyperparameters. The MLP and CNN models were designed to maximize their pattern recognition capabilities, while the RNN (LSTM) was optimized for processing temporal data. To assess their performance, we employed metrics such as precision, recall, and F1-score. The MLP and CNN models demonstrated exceptional accuracy, with scores of 0.98 across these metrics, indicating their effectiveness in feature extraction and pattern recognition. In contrast, the LSTM variant of RNN showed lower efficacy, with scores below 0.60, highlighting the challenges in handling sequential speech data. The results of this study shed light on the differing capabilities of these models in ASR. While the high accuracy of MLP and CNN suggests potential overfitting, the underperformance of LSTM underscores the necessity for further refinement in sequential data processing. This research contributes to the understanding of various machine learning approaches in ASR and paves the way for future investigations. We propose exploring hybrid model architectures and enhancing feature extraction methods to develop more sophisticated, real-world ASR systems. Additionally, our findings underscore the importance of considering model-specific strengths and limitations in ASR applications, guiding the direction of future research in this rapidly evolving field
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