12 citations · 49 across the 17 of their papers we have counts for
10 papers · 1 filter
Time-Varying Quasi-Closed-Phase Analysis for Accurate Formant Tracking in Speech Signals
Dhananjaya Gowda, Sudarsana Reddy Kadiri, Brad Story +1
In this paper, we propose a new method for the accurate estimation and tracking of formants in speech signals using time-varying quasi-closed-phase (TVQCP) analysis. Conventional f…
Refining a Deep Learning-based Formant Tracker using Linear Prediction Methods
Paavo Alku, Sudarsana Reddy Kadiri, Dhananjaya Gowda
In this study, formant tracking is investigated by refining the formants tracked by an existing data-driven tracker, DeepFormants, using the formants estimated in a model-driven ma…
Two-Pass End-to-End ASR Model Compression
Nauman Dawalatabad, Tushar Vatsal, Ashutosh Gupta +4
Speech recognition on smart devices is challenging owing to the small memory footprint. Hence small size ASR models are desirable. With the use of popular transducer-based models,…
Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
Dhananjaya Gowda, Bajibabu Bollepalli, Sudarsana Reddy Kadiri +1
Formant tracking is investigated in this study by using trackers based on dynamic programming (DP) and deep neural nets (DNNs). Using the DP approach, six formant estimation method…
Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages
Jiyeon Kim, Mehul Kumar, Dhananjaya Gowda +2
In this paper, we propose a three-stage training methodology to improve the speech recognition accuracy of low-resource languages. We explore and propose an effective combination o…
A comparison of streaming models and data augmentation methods for robust speech recognition
Jiyeon Kim, Mehul Kumar, Dhananjaya Gowda +2
In this paper, we present a comparative study on the robustness of two different online streaming speech recognition models: Monotonic Chunkwise Attention (MoChA) and Recurrent Neu…
Attention based on-device streaming speech recognition with large speech corpus
Kwangyoun Kim, Kyungmin Lee, Dhananjaya Gowda +10
In this paper, we present a new on-device automatic speech recognition (ASR) system based on monotonic chunk-wise attention (MoChA) models trained with large (> 10K hours) corpus.…
Improved Multi-Stage Training of Online Attention-based Encoder-Decoder Models
Abhinav Garg, Dhananjaya Gowda, Ankur Kumar +3
In this paper, we propose a refined multi-stage multi-task training strategy to improve the performance of online attention-based encoder-decoder (AED) models. A three-stage traini…
power-law nonlinearity with maximally uniform distribution criterion for improved neural network training in automatic speech recognition
Chanwoo Kim, Mehul Kumar, Kwangyoun Kim +1
In this paper, we describe the Maximum Uniformity of Distribution (MUD) algorithm with the power-law nonlinearity. In this approach, we hypothesize that neural network training wil…
end-to-end training of a large vocabulary end-to-end speech recognition system
Chanwoo Kim, Sungsoo Kim, Kwangyoun Kim +10
In this paper, we present an end-to-end training framework for building state-of-the-art end-to-end speech recognition systems. Our training system utilizes a cluster of Central Pr…