Search Results for author:
Found 12 papers, 2 papers with code
Promptformer: Prompted Conformer Transducer for ASR
Context cues carry information which can improve multi-turn interactions in automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Multi-View Frequency-Attention Alternative to CNN Frontends for Automatic Speech Recognition
Convolutional frontends are a typical choice for Transformer-based automatic speech recognition to preprocess the spectrogram, reduce its sequence length, and combine local information in time and frequency similarly.
Contextual-Utterance Training for Automatic Speech Recognition
Also, we propose a dual-mode contextual-utterance training technique for streaming automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multi-channel Opus compression for far-field automatic speech recognition with a fixed bitrate budget
Automatic speech recognition (ASR) in the cloud allows the use of larger models and more powerful multi-channel signal processing front-ends compared to on-device processing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR
Most approaches to multi-talker overlapped speech separation and recognition assume that the number of simultaneously active speakers is given, but in realistic situations, it is typically unknown.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
End-to-end training of time domain audio separation and recognition
The rising interest in single-channel multi-speaker speech separation sparked development of End-to-End (E2E) approaches to multi-speaker speech recognition.
Demystifying TasNet: A Dissecting Approach
In recent years time domain speech separation has excelled over frequency domain separation in single channel scenarios and noise-free environments.
SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel source separation and recognition
We present a multi-channel database of overlapping speech for training, evaluation, and detailed analysis of source separation and extraction algorithms: SMS-WSJ -- Spatialized Multi-Speaker Wall Street Journal.
Unsupervised training of a deep clustering model for multichannel blind source separation
We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable.
Unsupervised training of neural mask-based beamforming
In contrast to previous work on unsupervised training of neural mask estimators, our approach avoids the need for a possibly pre-trained teacher model entirely.
Directional Statistics and Filtering Using libDirectional
In this paper, we present libDirectional, a MATLAB library for directional statistics and directional estimation.
Neural network based spectral mask estimation for acoustic beamforming
The network training is independent of the number and the geometric configuration of the microphones.
