Search Results for author:
Found 16 papers, 7 papers with code
LibriSpeech-PC: Benchmark for Evaluation of Punctuation and Capitalization Capabilities of end-to-end ASR Models
Traditional automatic speech recognition (ASR) models output lower-cased words without punctuation marks, which reduces readability and necessitates a subsequent text processing model to convert ASR transcripts into a proper format.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Retrieval meets Long Context Large Language Models
Perhaps surprisingly, we find that LLM with 4K context window using simple retrieval-augmentation at generation can achieve comparable performance to finetuned LLM with 16K context window via positional interpolation on long context tasks, while taking much less computation.
A Chat About Boring Problems: Studying GPT-based text normalization
Through this new framework, we can identify strengths and weaknesses of GPT-based TN, opening opportunities for future work.
P-Flow: A Fast and Data-Efficient Zero-Shot TTS through Speech Prompting
P-Flow comprises a speech-prompted text encoder for speaker adaptation and a flow matching generative decoder for high-quality and fast speech synthesis.
SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings
Contextual spelling correction models are an alternative to shallow fusion to improve automatic speech recognition (ASR) quality given user vocabulary.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Automatic Heteronym Resolution Pipeline Using RAD-TTS Aligners
Grapheme-to-phoneme (G2P) transduction is part of the standard text-to-speech (TTS) pipeline.
Thutmose Tagger: Single-pass neural model for Inverse Text Normalization
The model is trained on the Google Text Normalization dataset and achieves state-of-the-art sentence accuracy on both English and Russian test sets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text Normalization
First, a non-deterministic WFST outputs all normalization candidates, and then a neural language model picks the best one -- similar to shallow fusion for automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
A Unified Transformer-based Framework for Duplex Text Normalization
In addition, we also create a cleaned dataset from the Spoken Wikipedia Corpora for German and report the performance of our systems on the dataset.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
SGD-QA: Fast Schema-Guided Dialogue State Tracking for Unseen Services
In this paper, we propose SGD-QA, a simple and extensible model for schema-guided dialogue state tracking based on a question answering approach.
A Toolbox for Construction and Analysis of Speech Datasets
Automatic Speech Recognition and Text-to-Speech systems are primarily trained in a supervised fashion and require high-quality, accurately labeled speech datasets.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
NeMo Inverse Text Normalization: From Development To Production
Inverse text normalization (ITN) converts spoken-domain automatic speech recognition (ASR) output into written-domain text to improve the readability of the ASR output.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Hi-Fi Multi-Speaker English TTS Dataset
This paper introduces a new multi-speaker English dataset for training text-to-speech models.
BioMegatron: Larger Biomedical Domain Language Model
There has been an influx of biomedical domain-specific language models, showing language models pre-trained on biomedical text perform better on biomedical domain benchmarks than those trained on general domain text corpora such as Wikipedia and Books.
Ranked #1 on
Named Entity Recognition (NER)
on BC5CDR-disease
A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset
Dialog State Tracking (DST) is one of the most crucial modules for goal-oriented dialogue systems.
Sentiment Classification using Images and Label Embeddings
In this project we analysed how much semantic information images carry, and how much value image data can add to sentiment analysis of the text associated with the images.
