LEAP System for SRE19 CTS Challenge -- Improvements and Error Analysis

The NIST CTS Speaker Recognition Challenge

The US National Institute of Standards and Technology (NIST) has been conducting a second iteration of the CTS challenge since August 2020. The current iteration of the CTS Challenge is a leaderboard-style speaker recognition evaluation using telephony data extracted from the unexposed portions of the Call My Net 2 (CMN2) and Multi-Language Speech (MLS) corpora collected by the LDC. The CTS Challenge is currently organized in a similar manner to the SRE19 CTS Challenge, offering only an open training condition using two evaluation subsets, namely Progress and Test. Unlike in the SRE19 Challeng…

LEAP Submission for the Third DIHARD Diarization Challenge

The LEAP submission for DIHARD-III challenge is described in this paper. The proposed system is composed of a speech bandwidth classifier, and diarization systems fine-tuned for narrowband and wideband speech separately. We use an end-to-end speaker diarization system for the narrowband conversational telephone speech recordings. For the wideband multi-speaker recordings, we use a neural embedding based clustering approach, similar to the baseline system. The embeddings are extracted from a time-delay neural network (called x-vectors) followed by the graph based path integral clustering (PIC) …

LEAP Submission to CHiME-6 ASR Challenge}

This paper reports the LEAP submission to the CHiME-6 challenge. The CHiME-6 Automatic Speech Recognition (ASR) challenge Track 1 involved the recognition of speech in noisy and reverberant acoustic conditions in home environments with multiple-party interactions. For the challenge submission, the LEAP system used extensive data augmentation and a factorized time-delay neural network (TDNN) architecture. We also explored a neural architecture that interleaved the TDNN layers with LSTM layers. The submitted system improved the Kaldi recipe by 2% in terms of relative word-error-rate improvements…

Joint domain adaptation and speech bandwidth extension using time-domain GANs for speaker verification

Speech systems developed for a particular choice of acoustic domain and sampling frequency do not translate easily to others. The usual practice is to learn domain adaptation and bandwidth extension models independently. Contrary to this, we propose to learn both tasks together. Particularly, we learn to map narrowband conversational telephone speech to wideband microphone speech. We developed parallel and non-parallel learning solutions which utilize both paired and unpaired data. First, we first discuss joint and disjoint training of multiple generative models for our tasks. Then, we propose…

SRIB-LEAP submission to Far-field Multi-Channel Speech Enhancement Challenge for Video Conferencing

This paper presents the details of the SRIB-LEAP submission to the ConferencingSpeech challenge 2021. The challenge involved the task of multi-channel speech enhancement to improve the quality of far field speech from microphone arrays in a video conferencing room. We propose a two stage method involving a beamformer followed by single channel enhancement. For the beamformer, we incorporated self-attention mechanism as inter-channel processing layer in the filter-and-sum network (FaSNet), an end-to-end time-domain beamforming system. The single channel speech enhancement is done in log spectra…

说话人未嵌入：面向长音频神经说话人日志的无嵌入方法

Speakers Unembedded: Embedding-free Approach to Long-form Neural Diarization

提出一种无需额外说话人嵌入的局部与全局EEND框架，解决了长音频多说话人泛化问题，在Callhome和RT03-CTS数据集上显著降低DER并减少计算复杂度。