Search Results for author:
Found 15 papers, 2 papers with code
Alternating Weak Triphone/BPE Alignment Supervision from Hybrid Model Improves End-to-End ASR
In this paper, alternating weak triphone/BPE alignment supervision is proposed to improve end-to-end model training.
Chunked Attention-based Encoder-Decoder Model for Streaming Speech Recognition
We study a streamable attention-based encoder-decoder model in which either the decoder, or both the encoder and decoder, operate on pre-defined, fixed-size windows called chunks.
Improving Language Model Integration for Neural Machine Translation
The integration of language models for neural machine translation has been extensively studied in the past.
Take the Hint: Improving Arabic Diacritization with Partially-Diacritized Text
Automatic Arabic diacritization is useful in many applications, ranging from reading support for language learners to accurate pronunciation predictor for downstream tasks like speech synthesis.
Robust Knowledge Distillation from RNN-T Models With Noisy Training Labels Using Full-Sum Loss
Soft distillation is another popular KD method that distills the output logits of the teacher model.
Analyzing And Improving Neural Speaker Embeddings for ASR
By further adding neural speaker embeddings, we gain additional ~3% relative WER improvement on Hub5'00.
Enhancing and Adversarial: Improve ASR with Speaker Labels
Detailed analysis and experimental verification are conducted to show the optimal positions in the ASR neural network (NN) to apply speaker enhancing and adversarial training.
Development of Hybrid ASR Systems for Low Resource Medical Domain Conversational Telephone Speech
Language barriers present a great challenge in our increasingly connected and global world.
Improving the Training Recipe for a Robust Conformer-based Hybrid Model
In this work, we investigate various methods for speaker adaptive training (SAT) based on feature-space approaches for a conformer-based acoustic model (AM) on the Switchboard 300h dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Conformer-based Hybrid ASR System for Switchboard Dataset
The recently proposed conformer architecture has been successfully used for end-to-end automatic speech recognition (ASR) architectures achieving state-of-the-art performance on different datasets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Automatic Learning of Subword Dependent Model Scales
We show on the LibriSpeech (LBS) and Switchboard (SWB) corpora that the model scales for a combination of attentionbased encoder-decoder acoustic model and language model can be learned as effectively as with manual tuning.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Acoustic Data-Driven Subword Modeling for End-to-End Speech Recognition
Subword units are commonly used for end-to-end automatic speech recognition (ASR), while a fully acoustic-oriented subword modeling approach is somewhat missing.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Investigating Methods to Improve Language Model Integration for Attention-based Encoder-Decoder ASR Models
Attention-based encoder-decoder (AED) models learn an implicit internal language model (ILM) from the training transcriptions.
Comparing the Benefit of Synthetic Training Data for Various Automatic Speech Recognition Architectures
We achieve a final word-error-rate of 3. 3%/10. 0% with a hybrid system on the clean/noisy test-sets, surpassing any previous state-of-the-art systems on Librispeech-100h that do not include unlabeled audio data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A systematic comparison of grapheme-based vs. phoneme-based label units for encoder-decoder-attention models
Following the rationale of end-to-end modeling, CTC, RNN-T or encoder-decoder-attention models for automatic speech recognition (ASR) use graphemes or grapheme-based subword units based on e. g. byte-pair encoding (BPE).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
