speech recognition research paper

a Author to whom correspondence should be addressed. Page 2. 2. Measuring how well human listeners recognize speech under varying environmental. 1.

par.nsf.gov·648.8 KB

We investigate the efficacy of deep neural networks on speech recognition. Specif- ically, we implement an end-to-end deep learning system that utilizes ...

cs224d.stanford.edu·584.2 KB

For this, we investigate patterns in child speech, adult speech and re- late these in order to evaluate from where children receive their linguistic building.

www.lingref.com·558.8 KB

SpeechLM consists of three key components: speech tokenizer, language model, and token-to- speech synthesizer (vocoder) (see Figure 1). The.

aclanthology.org·537.6 KB

This work presents our end-to-end (E2E) automatic speech recognition (ASR) model targetting at robust speech recog- nition, called Integraded speech ...

www.isca-archive.org·389.5 KB

This paper outlines the strategies for merging the hybrid Kaldi ASR model and the end-to-end XLS-R model with the help of confidence scores.

aclanthology.org·358.9 KB

This paper addresses end-to-end automatic speech recognition (ASR) for long audio record- ings such as lecture and conversational speeches. Most ...

www.merl.com·351.4 KB

This paper reviews past work comparing modern speech recognition systems and humans to determine how far recent dramatic advances in technology have ...

www.ee.columbia.edu·343.7 KB

Using the new learning methods, several different research groups have shown that DNNs can outperform. GMMs at acoustic modeling for speech ...

research.google.com·267.0 KB

We present in this paper an alternative construction, based on word embeddings, that can be used to score any new word for any acoustic sequence, and that hence.

research.google.com·233.0 KB

Currently, three popular end-to-end approaches are Con- nectionist Temporal Classification (CTC), RNN-Transducers and attention-based methods [8]. CTC ...

danielpovey.com·223.9 KB

A typical ASR system uses Hidden Markov Models. (HMMs) to model the sequential structure of speech signals, with each HMM state using a mixture of Gaussians to ...

www.cs.toronto.edu·222.6 KB

The study will examine AI-SRT's effectiveness for enhancing pronunciation and speaking skills, advancing English language learning. This could optimize language ...

files.eric.ed.gov·202.9 KB

The latest technology is "continuous speech." Natural or conversational speech is recorded, and phoneme recognition is used to recognize streams ...

journal.ahima.org·183.8 KB

Two language techniques you can use at home are Parallel Talk and Self-Talk. Parallel Talk. Parallel Talk is when you talk about what YOUR CHILD is doing, ...

weespeech.com·113.6 KB

In this paper, we concentrate on three aspects of speech recognition: acoustic modeling, front-end processing and pro- nunciation modeling for building robust ...

sail.usc.edu·78.7 KB

Methods for speaker adaptive acoustic modeling proved to be effective, in particular for training acoustic models on a mix- ture of adult and children's speech, ...

www.isca-archive.org·71.0 KB

The proposed systematic review in this article critically examines the impact of transformer models on speech recognition, covering the ...

ieeexplore.ieee.org·2.4 KB

Abstract—In the last decade of automatic speech recognition. (ASR) research, the introduction of deep learning has brought considerable reductions in word ...

ieeexplore.ieee.org·2.4 KB

Classic speech technologies rely on cascade modeling, i.e. transcribing speech to text with an Automatic Speech Recognition (ASR) system, ...

theses.hal.science