DNN-based speech bandwidth expansion and its application to adding high-frequency missing features for automatic speech recognition of narrowband speech

Li, Kehuang; Huang, Zhen; Xu, Yong; Lee, Chin‐Hui

doi:10.21437/interspeech.2015-555

Cited by 37 publications

(23 citation statements)

References 27 publications

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“…Depending on whether the problem is viewed as a regression or classification problem, the labels li are chosen differently. BWE is typically treated as a regression problem and the mapping function is estimated under the MMSE criterion as follows [4,5,13]…”

Section: Bandwidth Extensionmentioning

confidence: 99%

Large-Scale Mixed-Bandwidth Deep Neural Network Acoustic Modeling for Automatic Speech Recognition

Mac¹,

Cui²,

Zhang³

et al. 2019

Interspeech 2019

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In automatic speech recognition (ASR), wideband (WB) and narrowband (NB) speech signals with different sampling rates typically use separate acoustic models. Therefore mixed-bandwidth (MB) acoustic modeling has important practical values for ASR system deployment. In this paper, we extensively investigate large-scale MB deep neural network acoustic modeling for ASR using 1,150 hours of WB data and 2,300 hours of NB data. We study various MB strategies including downsampling, upsampling and bandwidth extension for MB acoustic modeling and evaluate their performance on 8 diverse WB and NB test sets from various application domains. To deal with the large amounts of training data, distributed training is carried out on multiple GPUs using synchronous data parallelism.

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Section: Bandwidth Extensionmentioning

confidence: 99%

Large-Scale Mixed-Bandwidth Deep Neural Network Acoustic Modeling for Automatic Speech Recognition

Mac¹,

Cui²,

Zhang³

et al. 2019

Interspeech 2019

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“…Audio super-resolution, neural upsampling, or bandwidth extension is the task of generating high sampling rate audio signals with full frequency bandwidth from low sampling rate signals. There have been several works that applied deep neural networks to audio super-resolution [1,2,3,4,5,6,7].…”

Section: Introductionmentioning

confidence: 99%

NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling

Lee¹,

Han²

2021

Interspeech 2021

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In this work, we introduce NU-Wave, the first neural audio upsampling model to produce waveforms of sampling rate 48kHz from coarse 16kHz or 24kHz inputs, while prior works could generate only up to 16kHz. NU-Wave is the first diffusion probabilistic model for audio super-resolution which is engineered based on neural vocoders. NU-Wave generates high-quality audio that achieves high performance in terms of signal-to-noise ratio (SNR), log-spectral distance (LSD), and accuracy of the ABX test. In all cases, NU-Wave outperforms the baseline models despite the substantially smaller model capacity (3.0M parameters) than baselines (5.4-21%). The audio samples of our model are available at https://mindslab-ai.github.io/nuwave, and the code will be made available soon.

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“…Previous approaches to this task include Gaussian mixture models [8,9], linear predictive coding [10] and neural networks [11]. Learning-based methods perform better in this context because they can capture sophisticated domain-specific information.…”

Section: Introductionmentioning

confidence: 99%

WSRGlow: A Glow-Based Waveform Generative Model for Audio Super-Resolution

Zhang¹,

Ren

Xu³

et al. 2021

Interspeech 2021

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Audio super-resolution is the task of constructing a highresolution (HR) audio from a low-resolution (LR) audio by adding the missing band. Previous methods based on convolutional neural networks and mean squared error training objective have relatively low performance, while adversarial generative models are difficult to train and tune. Recently, normalizing flow has attracted a lot of attention for its high performance, simple training and fast inference. In this paper, we propose WSRGlow, a Glow-based waveform generative model to perform audio super-resolution. Specifically, 1) we integrate WaveNet and Glow to directly maximize the exact likelihood of the target HR audio conditioned on LR information; and 2) to exploit the audio information from low-resolution audio, we propose an LR audio encoder and an STFT encoder, which encode the LR information from the time domain and frequency domain respectively. The experimental results show that the proposed model is easier to train and outperforms the previous works in terms of both objective and perceptual quality. WSRGlow is also the first model to produce 48kHz waveforms from 12kHz LR audio. Audio samples are available at https://zkx06111.github.io/wsrglow/.

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DNN-based speech bandwidth expansion and its application to adding high-frequency missing features for automatic speech recognition of narrowband speech

Cited by 37 publications

References 27 publications

Large-Scale Mixed-Bandwidth Deep Neural Network Acoustic Modeling for Automatic Speech Recognition

Large-Scale Mixed-Bandwidth Deep Neural Network Acoustic Modeling for Automatic Speech Recognition

NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling

WSRGlow: A Glow-Based Waveform Generative Model for Audio Super-Resolution

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