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Found 23 papers, 0 papers with code
Two-pass Endpoint Detection for Speech Recognition
Endpoint (EP) detection is a key component of far-field speech recognition systems that assist the user through voice commands.
Cross-utterance ASR Rescoring with Graph-based Label Propagation
We propose a novel approach for ASR N-best hypothesis rescoring with graph-based label propagation by leveraging cross-utterance acoustic similarity.
Leveraging Redundancy in Multiple Audio Signals for Far-Field Speech Recognition
We augment the MC fusion networks to a conformer transducer model and train it in an end-to-end fashion.
Guided contrastive self-supervised pre-training for automatic speech recognition
Contrastive Predictive Coding (CPC) is a representation learning method that maximizes the mutual information between intermediate latent representations and the output of a given model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Reducing Geographic Disparities in Automatic Speech Recognition via Elastic Weight Consolidation
A popular approach is to fine-tune the model with data from regions where the ASR model has a higher word error rate (WER).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
VADOI:Voice-Activity-Detection Overlapping Inference For End-to-end Long-form Speech Recognition
Results show that the proposed method can achieve a 20% relative computation cost reduction on Librispeech and Microsoft Speech Language Translation long-form corpus while maintaining the WER performance when comparing to the best performing overlapping inference algorithm.
SynthASR: Unlocking Synthetic Data for Speech Recognition
Our observations show that SynthASR holds great promise in training the state-of-the-art large-scale E2E ASR models for new applications while reducing the costs and dependency on production data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Do You Listen with One or Two Microphones? A Unified ASR Model for Single and Multi-Channel Audio
An ASR model that operates on both primary and auxiliary data can achieve better accuracy compared to a primary-only solution; and a model that can serve both primary-only (PO) and primary-plus-auxiliary (PPA) modes is highly desirable.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Attention-based Neural Beamforming Layers for Multi-channel Speech Recognition
The end-to-end 2D Conv-Attention model is compared with a multi-head self-attention and superdirective-based neural beamformers.
Wav2vec-C: A Self-supervised Model for Speech Representation Learning
However, the quantization process is regularized by an additional consistency network that learns to reconstruct the input features to the wav2vec 2. 0 network from the quantized representations in a way similar to a VQ-VAE model.
REDAT: Accent-Invariant Representation for End-to-End ASR by Domain Adversarial Training with Relabeling
Accents mismatching is a critical problem for end-to-end ASR.
Efficient minimum word error rate training of RNN-Transducer for end-to-end speech recognition
Unlike previous work on this topic, which performs on-the-fly limited-size beam-search decoding and generates alignment scores for expected edit-distance computation, in our proposed method, we re-calculate and sum scores of all the possible alignments for each hypothesis in N-best lists.
Streaming End-to-End Bilingual ASR Systems with Joint Language Identification
Experiments show that for English-Spanish, the bilingual joint ASR-LID architecture matches monolingual ASR and acoustic-only LID accuracies.
Multi-view Frequency LSTM: An Efficient Frontend for Automatic Speech Recognition
Acoustic models in real-time speech recognition systems typically stack multiple unidirectional LSTM layers to process the acoustic frames over time.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Streaming Language Identification using Combination of Acoustic Representations and ASR Hypotheses
A common approach to solve multilingual speech recognition is to run multiple monolingual ASR systems in parallel and rely on a language identification (LID) component that detects the input language.
DiPCo -- Dinner Party Corpus
We present a speech data corpus that simulates a "dinner party" scenario taking place in an everyday home environment.
Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning
For real-world speech recognition applications, noise robustness is still a challenge.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
LSTM-based Whisper Detection
We prove that, with enough data, the LSTM model is indeed as capable of learning whisper characteristics from LFBE features alone compared to a simpler MLP model that uses both LFBE and features engineered for separating whisper and normal speech.
Device-directed Utterance Detection
In this work, we propose a classifier for distinguishing device-directed queries from background speech in the context of interactions with voice assistants.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Estimating parameters of nonlinear systems using the elitist particle filter based on evolutionary strategies
In this article, we present the elitist particle filter based on evolutionary strategies (EPFES) as an efficient approach for nonlinear system identification.
The NLMS algorithm with time-variant optimum stepsize derived from a Bayesian network perspective
In this article, we derive a new stepsize adaptation for the normalized least mean square algorithm (NLMS) by describing the task of linear acoustic echo cancellation from a Bayesian network perspective.
Spatial Diffuseness Features for DNN-Based Speech Recognition in Noisy and Reverberant Environments
We propose a spatial diffuseness feature for deep neural network (DNN)-based automatic speech recognition to improve recognition accuracy in reverberant and noisy environments.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition
This article provides a unifying Bayesian network view on various approaches for acoustic model adaptation, missing feature, and uncertainty decoding that are well-known in the literature of robust automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
