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Found 15 papers, 1 papers with code
Time-Varying Quasi-Closed-Phase Analysis for Accurate Formant Tracking in Speech Signals
Formant tracking experiments with a wide variety of synthetic and natural speech signals show that the proposed TVQCP method performs better than conventional and popular formant tracking tools, such as Wavesurfer and Praat (based on dynamic programming), the KARMA algorithm (based on Kalman filtering), and DeepFormants (based on deep neural networks trained in a supervised manner).
end-to-end training of a large vocabulary end-to-end speech recognition system
Our end-to-end speech recognition system built using this training infrastructure showed a 2. 44 % WER on test-clean of the LibriSpeech test set after applying shallow fusion with a Transformer language model (LM).
power-law nonlinearity with maximally uniform distribution criterion for improved neural network training in automatic speech recognition
With the power function-based MUD, we apply a power-function based nonlinearity where power function coefficients are chosen to maximize the likelihood assuming that nonlinearity outputs follow the uniform distribution.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Improved Multi-Stage Training of Online Attention-based Encoder-Decoder Models
In this paper, we propose a refined multi-stage multi-task training strategy to improve the performance of online attention-based encoder-decoder (AED) models.
Attention based on-device streaming speech recognition with large speech corpus
In this paper, we present a new on-device automatic speech recognition (ASR) system based on monotonic chunk-wise attention (MoChA) models trained with large (> 10K hours) corpus.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
A review of on-device fully neural end-to-end automatic speech recognition algorithms
Conventional speech recognition systems comprise a large number of discrete components such as an acoustic model, a language model, a pronunciation model, a text-normalizer, an inverse-text normalizer, a decoder based on a Weighted Finite State Transducer (WFST), and so on.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Streaming end-to-end speech recognition with jointly trained neural feature enhancement
In this paper, we present a streaming end-to-end speech recognition model based on Monotonic Chunkwise Attention (MoCha) jointly trained with enhancement layers.
Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages
To improve the accuracy of a low-resource Italian ASR, we leverage a well-trained English model, unlabeled text corpus, and unlabeled audio corpus using transfer learning, TTS augmentation, and SSL respectively.
A comparison of streaming models and data augmentation methods for robust speech recognition
However, we observe that training of MoChA models seems to be more sensitive to various factors such as the characteristics of training sets and the incorporation of additional augmentations techniques.
Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
Formant tracking is investigated in this study by using trackers based on dynamic programming (DP) and deep neural nets (DNNs).
Two-Pass End-to-End ASR Model Compression
With the use of popular transducer-based models, it has become possible to practically deploy streaming speech recognition models on small devices [1].
Multi-stage Progressive Compression of Conformer Transducer for On-device Speech Recognition
Knowledge distillation (KD) is a popular model compression approach that has shown to achieve smaller model size with relatively lesser degradation in the model performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Mitigating the Exposure Bias in Sentence-Level Grapheme-to-Phoneme (G2P) Transduction
Text-to-Text Transfer Transformer (T5) has recently been considered for the Grapheme-to-Phoneme (G2P) transduction.
Refining a Deep Learning-based Formant Tracker using Linear Prediction Methods
The results indicated that the data-driven DeepFormants trackers outperformed the conventional trackers and that the best performance was obtained by refining the formants predicted by DeepFormants using QCP-FB analysis.
Data-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech
Grapheme-to-Phoneme (G2P) is an essential first step in any modern, high-quality Text-to-Speech (TTS) system.
