Search Results for author: Chandra Dhir

Found 9 papers, 0 papers with code

Continual Learning for End-to-End ASR by Averaging Domain Experts

no code implementations12 May 2023 Peter Plantinga, Jaekwon Yoo, Chandra Dhir

Our experiments show that a simple linear interpolation of several models' parameters, each fine-tuned from the same generalist model, results in a single model that performs well on all tested data.

Automatic Speech Recognition Continual Learning +2

Multi-task Learning with Cross Attention for Keyword Spotting

no code implementations15 Jul 2021 Takuya Higuchi, Anmol Gupta, Chandra Dhir

In this approach, an output of an acoustic model is split into two branches for the two tasks, one for phoneme transcription trained with the ASR data and one for keyword classification trained with the KWS data.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Streaming Transformer for Hardware Efficient Voice Trigger Detection and False Trigger Mitigation

no code implementations14 May 2021 Vineet Garg, Wonil Chang, Siddharth Sigtia, Saurabh Adya, Pramod Simha, Pranay Dighe, Chandra Dhir

We propose a streaming transformer (TF) encoder architecture, which progressively processes incoming audio chunks and maintains audio context to perform both VTD and FTM tasks using only acoustic features.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Optimize what matters: Training DNN-HMM Keyword Spotting Model Using End Metric

no code implementations2 Nov 2020 Ashish Shrivastava, Arnav Kundu, Chandra Dhir, Devang Naik, Oncel Tuzel

The DNN, in prior methods, is trained independent of the HMM parameters to minimize the cross-entropy loss between the predicted and the ground-truth state probabilities.

Keyword Spotting

Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech Synthesis

no code implementations9 Mar 2020 Ting-yao Hu, Ashish Shrivastava, Oncel Tuzel, Chandra Dhir

We present a method to generate speech from input text and a style vector that is extracted from a reference speech signal in an unsupervised manner, i. e., no style annotation, such as speaker information, is required.

Speech Synthesis

Cannot find the paper you are looking for? You can Submit a new open access paper.