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speech-1

Acoustic Modelling

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

End-to-end attention-based large vocabulary speech recognition

Dmitriy Serdyuk, Yoshua Bengio, Dzmitry Bahdanau, J. Chorowski, Philemon Brakel•Mon Aug 17 2015

This work investigates an alternative method for sequence modelling based on an attention mechanism that allows a Recurrent Neural Network (RNN) to learn alignments between sequences of input frames and output labels.

1193

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Phonemic Transcription of Low-Resource Tonal Languages

Graham Neubig, Trevor Cohn, Oliver Adams, Alexis Michaud•Tue Dec 05 2017

The use of a neural network architecture with the connectionist temporal classification loss function for phonemic and tonal transcription in a language documentation setting is explored and the method's promise in improving efficiency, minimizing typographical errors, and maintaining the transcription's faithfulness to the acoustic signal is shown.

16 0

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WGANSing: A Multi-Voice Singing Voice Synthesizer Based on the Wasserstein-GAN

Pritish Chandna, Merlijn Blaauw, J. Bonada, E. Gómez•Mon Mar 25 2019

A deep neural network based singing voice synthesizer, inspired by the Deep Convolutions Generative Adversarial Networks (DCGAN) architecture and optimized using the Wasserstein-GAN algorithm, which facilitates the modelling of the large variability of pitch in the singing voice.

66 0

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GIBBONFINDR: An R package for the detection and classification of acoustic signals

H. Klinck, D. Clink•Wed Jun 05 2019

The new, open-source R package GIBBONFINDR is described which has functions for detection, classification and visualization of acoustic signals using a variety of readily available machine learning algorithms in the R programming environment.

7 0

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Acoustic Model Adaptation from Raw Waveforms with Sincnet

S. Renals, Ondrej Klejch, P. Bell, Joachim Fainberg, Erfan Loweimi•Sun Sep 29 2019

It is shown that the parameterisation of the SincNet layer is well suited for adaptation in practice: it can efficiently adapt with a very small number of parameters, producing error rates comparable to techniques using orders of magnitude more parameters.

15 0

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Multilingual Bottleneck Features for Improving ASR Performance of Code-Switched Speech in Under-Resourced Languages

Trideba Padhi, A. Biswas, F. D. Wet, E. V. D. Westhuizen, T. Niesler•Fri Oct 30 2020

This work explores the benefits of using multilingual bottleneck features (mBNF) in acoustic modelling for the automatic speech recognition of code-switched speech in African languages and shows that the inclusion of the mBNF features leads to clear performance improvements over a baseline trained without them.

4 0

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Matcha-TTS: A Fast TTS Architecture with Conditional Flow Matching

G. Henter, J. Beskow, Shivam Mehta, Éva Székely, Ruibo Tu•Tue Sep 05 2023

Matcha-TTS, a new encoder-decoder architecture for speedy TTS acoustic modelling, trained using optimal-transport conditional flow matching (OT-CFM), is introduced, an ODE-based decoder capable of high output quality in fewer synthesis steps than models trained using score matching.

185 0

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SonoTraceLab—A Raytracing-Based Acoustic Modeling System for Simulating Echolocation Behavior of Bats

W. Jansen, Jan Steckel•Sun Mar 10 2024

SonoTraceLab is an open-source software package for simulating both technical as well as biological echolocation systems in complex scenes, which can drastically increase insights into the nature of biological echolocation systems, while reducing the time- and material complexity of performing them.

2 0

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Acoustic Modelling | State-of-the-Art