Bandwidth extension of narrowband speech

Recently, 4G mobile phone systems have been designed to process wideband speech signals whose sampling frequency is 16 kHz. However, most part of mobile and classical phone network, and current 3G mobile phones, still process narrowband speech signals whose sampling frequency is 8 kHz. During next future, all these systems must be living together. Therefore, sometimes a wideband speech signal (with a bandwidth up to 7,2 kHz) should be estimated from an available narrowband one (whose frequency band is 300-3400 Hz). In this work, different techniques of audio bandwidth extension have been implemented and evaluated. First, a simple non-model-based algorithm (interpolation algorithm) has been implemented. Second, a model-based algorithm (linear mapping) have been designed and evaluated in comparison to previous one. Several CMOS (Comparison Mean Opinion Score) [6] listening tests show that performance of Linear Mapping algorithm clearly overcomes the other one. Results of these tests are very close to those corresponding to original wideband speech signal.Postprint (published version

State-of-the-art Speech Recognition With Sequence-to-Sequence Models

Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network. In previous work, we have shown that such architectures are comparable to state-of-theart ASR systems on dictation tasks, but it was not clear if such architectures would be practical for more challenging tasks such as voice search. In this work, we explore a variety of structural and optimization improvements to our LAS model which significantly improve performance. On the structural side, we show that word piece models can be used instead of graphemes. We also introduce a multi-head attention architecture, which offers improvements over the commonly-used single-head attention. On the optimization side, we explore synchronous training, scheduled sampling, label smoothing, and minimum word error rate optimization, which are all shown to improve accuracy. We present results with a unidirectional LSTM encoder for streaming recognition. On a 12, 500 hour voice search task, we find that the proposed changes improve the WER from 9.2% to 5.6%, while the best conventional system achieves 6.7%; on a dictation task our model achieves a WER of 4.1% compared to 5% for the conventional system.Comment: ICASSP camera-ready versio