Search Results for author:
Found 27 papers, 2 papers with code
Extending Multilingual Speech Synthesis to 100+ Languages without Transcribed Data
Without any transcribed speech in a new language, this TTS model can generate intelligible speech in >30 unseen languages (CER difference of <10% to ground truth).
High-precision Voice Search Query Correction via Retrievable Speech-text Embedings
In this paper, we eliminate the hypothesis-audio mismatch problem by querying the correction database directly using embeddings derived from the utterance audio; the embeddings of the utterance audio and candidate corrections are produced by multimodal speech-text embedding networks trained to place the embedding of the audio of an utterance and the embedding of its corresponding textual transcript close together.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Using Text Injection to Improve Recognition of Personal Identifiers in Speech
We use text-injection to improve the recognition of PII categories by including fake textual substitutes of PII categories in the training data using a text injection method.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
O-1: Self-training with Oracle and 1-best Hypothesis
O-1 achieves 13\% to 25\% relative improvement over EMBR on the various datasets that SpeechStew comprises of, and a 12\% relative gap reduction with respect to the oracle WER over EMBR training on the in-house dataset.
Improving Joint Speech-Text Representations Without Alignment
The last year has seen astonishing progress in text-prompted image generation premised on the idea of a cross-modal representation space in which the text and image domains are represented jointly.
Understanding Shared Speech-Text Representations
Recently, a number of approaches to train speech models by incorpo-rating text into end-to-end models have been developed, with Mae-stro advancing state-of-the-art automatic speech recognition (ASR)and Speech Translation (ST) performance.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages
We introduce the Universal Speech Model (USM), a single large model that performs automatic speech recognition (ASR) across 100+ languages.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
JEIT: Joint End-to-End Model and Internal Language Model Training for Speech Recognition
We propose JEIT, a joint end-to-end (E2E) model and internal language model (ILM) training method to inject large-scale unpaired text into ILM during E2E training which improves rare-word speech recognition.
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech
This paper proposes Virtuoso, a massively multilingual speech-text joint semi-supervised learning framework for text-to-speech synthesis (TTS) models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR
Data augmentation is a ubiquitous technique used to provide robustness to automatic speech recognition (ASR) training.
Ranked #1 on
Speech Recognition
on CHiME-6 eval
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR
First, we show that by combining speech representations with byte-level text representations and use of language embeddings, we can dramatically reduce the Character Error Rate (CER) on languages with no supervised speech from 64. 8\% to 30. 8\%, a relative reduction of 53\%.
Non-Parallel Voice Conversion for ASR Augmentation
For ASR augmentation, it is necessary that the VC model be robust to a wide range of input speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Accented Speech Recognition: Benchmarking, Pre-training, and Diverse Data
However, there are not enough data sets for accented speech, and for the ones that are already available, more training approaches need to be explored to improve the quality of accented speech recognition.
MAESTRO: Matched Speech Text Representations through Modality Matching
Self-supervised learning from speech signals aims to learn the latent structure inherent in the signal, while self-supervised learning from text attempts to capture lexical information.
A Scalable Model Specialization Framework for Training and Inference using Submodels and its Application to Speech Model Personalization
We also show that learning a speaker-embedding space can scale further and reduce the amount of personalization training data required per speaker.
Ask2Mask: Guided Data Selection for Masked Speech Modeling
They treat all unsupervised speech samples with equal weight, which hinders learning as not all samples have relevant information to learn meaningful representations.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Injecting Text in Self-Supervised Speech Pretraining
The proposed method, tts4pretrain complements the power of contrastive learning in self-supervision with linguistic/lexical representations derived from synthesized speech, effectively learning from untranscribed speech and unspoken text.
Generating diverse and natural text-to-speech samples using a quantized fine-grained VAE and auto-regressive prosody prior
Recent neural text-to-speech (TTS) models with fine-grained latent features enable precise control of the prosody of synthesized speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Speech Recognition with Augmented Synthesized Speech
Recent success of the Tacotron speech synthesis architecture and its variants in producing natural sounding multi-speaker synthesized speech has raised the exciting possibility of replacing expensive, manually transcribed, domain-specific, human speech that is used to train speech recognizers.
Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloning
We present a multispeaker, multilingual text-to-speech (TTS) synthesis model based on Tacotron that is able to produce high quality speech in multiple languages.
Joint Modeling of Accents and Acoustics for Multi-Accent Speech Recognition
The performance of automatic speech recognition systems degrades with increasing mismatch between the training and testing scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
End-to-End ASR-free Keyword Search from Speech
The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to reconstruct the audio through a finite-dimensional representation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
RankDCG: Rank-Ordering Evaluation Measure
We propose a new measure, a modification of the popular nDCG algorithm, named rankDCG, that addresses these problems.
CUNY Systems for the Query-by-Example Search on Speech Task at MediaEval 2015
This paper describes two query-by-example systems developed by Speech Lab, Queens College (CUNY).
Ranked #49 on
Keyword Spotting
on QUESST
