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Found 12 papers, 0 papers with code
Effective internal language model training and fusion for factorized transducer model
However, even with the adoption of factorized transducer models, limited improvement has been observed compared to shallow fusion.
Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient Pruning of A Multilingual ASR Model
In this work, we propose the use of an adaptive masking approach in two scenarios for pruning a multilingual ASR model efficiently, each resulting in sparse monolingual models or a sparse multilingual model (named as Dynamic ASR Pathways).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Prompting Large Language Models with Speech Recognition Abilities
Furthermore, we perform ablation studies to investigate whether the LLM can be completely frozen during training to maintain its original capabilities, scaling up the audio encoder, and increasing the audio encoder striding to generate fewer embeddings.
Abstractive Text Summarization
Automatic Speech Recognition
+4
Improving Fast-slow Encoder based Transducer with Streaming Deliberation
Experiments on Librispeech and in-house data show relative WER reductions (WERRs) from 3% to 5% with a slight increase in model size and negligible extra token emission latency compared with fast-slow encoder based transducer.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Biased Self-supervised learning for ASR
Furthermore, this paper proposes a variant of MPPT that allows low-footprint streaming models to be trained effectively by computing the MPPT loss on masked and unmasked frames.
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.
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.
Variable frame rate-based data augmentation to handle speaking-style variability for automatic speaker verification
For instance, when enrolled with conversation utterances, the EER increased to 3. 03%, 2. 96% and 22. 12% when tested on read, narrative, and pet-directed speech, respectively.
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.
Singing voice conversion with non-parallel data
F0 and aperiodic are obtained through the original singing voice, and used with acoustic features to reconstruct the target singing voice through a vocoder.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A spelling correction model for end-to-end speech recognition
Attention-based sequence-to-sequence models for speech recognition jointly train an acoustic model, language model (LM), and alignment mechanism using a single neural network and require only parallel audio-text pairs.
Deep neural network based i-vector mapping for speaker verification using short utterances
Experimental results using the NIST SRE 2010 dataset show that both methods provide significant improvement and result in a max of 28. 43% relative improvement in Equal Error Rates from a baseline system, when using deep encoder with residual blocks and adding an additional phoneme vector.
