Mitchell McLaren

Deep neural network (DNN)-based speaker embeddings have resulted in new, state-of-the-art text-independent speaker recognition technology. However, very limited effort has been made to understand DNN speaker embeddings. In this study, our aim is analyzing the behavior of the speaker recognition systems based on speaker embeddings toward different front-end features, including the standard Mel frequency cepstral coefficients (MFCC), as well as power normalized cepstral coefficients (PNCC), and perceptual linear prediction (PLP). Using a speaker recognition system based on DNN speaker embeddings and probabilistic linear discriminant analysis (PLDA), we compared different approaches to leveraging complementary information using score-, embeddings-, and feature-level combination. We report our results for Speakers in the Wild (SITW) and NIST SRE 2016 datasets. We found that first and second embeddings layers are complementary in nature. By applying score and embedding-level fusion we demonstrate relative improvements in equal error rate of 17% on NIST SRE 2016 and 10% on SITW over the baseline system.

This article focuses on speaker recognition using speech acquired using a single distant or far-field microphone in an indoors environment. This study differs from the majority of speaker recognition research, which focuses on speech acquisition over short distances, such as when using a telephone handset or mobile device or far-field microphone arrays, for which beamforming can enhance distant speech signals. We use two large-scale corpora collected by retransmitting speech data in reverberant environments with multiple microphones placed at different distances. We first characterize three different speaker recognition systems ranging from a traditional universal background model (UBM) i-vector system to a state-of-the-art deep neural network (DNN) speaker embedding system with a probabilistic linear discriminant analysis (PLDA) back-end. We then assess the impact of microphone distance and placement, background noise, and loudspeaker orientation on the performance of speaker recognition system for distant speech data. We observe that the recently introduced DNN speaker embedding based systems are far more robust compared to i-vector based systems, providing a significant relative improvement of up to 54% over the baseline UBM i-vector system, and 45.5% over prior DNN-based speaker recognition technology.

With the recent introduction of speaker embeddings for text-independent speaker recognition, many fundamental questions require addressing in order to fast-track the development of this new era of technology. Of particular interest is the ability of the speaker embeddings network to leverage artificially degraded data at a far greater rate beyond prior technologies, even in the evaluation of naturally
degraded data. In this study, we aim to explore some of the fundamental requirements for building a good speaker embeddings extractor. We analyze the impact of voice activity detection, types of degradation,
the amount of degraded data, and number of speakers required for a good network. These aspects are analyzed over a large set of 11 conditions from 7 evaluation datasets. We lay out a set of recommendations for training the network based on the observed trends. By applying these recommendations to enhance the default recipe provided in the Kaldi toolkit, a significant gain of 13-21% on the Speakers in the Wild and NIST SRE’16 datasets is achieved.

Multi-domain language recognition involves the application of a language identification (LID) system to identify languages in more than one domain. This problem was the focus of the recent NIST LRE 2017, and this article presents the findings from the SRI team during system development for the evaluation. Approaches found to provide robustness in multi-domain LID include a domain-and-language-weighted Gaussian backend classifier, duration-aware calibration, and a source normalized multi-resolution neural network backend. The recently developed speaker embeddings technology is also applied to the task of language recognition, showing great potential for future LID research.

In this paper, we investigate several automatic transcription schemes for using raw bilingual broadcast news data in semi-supervised bilingual acoustic model training. Specifically, we compare the transcription quality provided by a bilingual ASR system with another system performing language diarization at the front-end followed by two monolingual ASR systems chosen based on the assigned language label. Our research focuses on the Frisian-Dutch code-switching (CS) speech that is extracted from the archives of a local radio broadcaster. Using 11 hours of manually transcribed Frisian speech as a reference, we aim to increase the amount of available training data by using these automatic transcription techniques. By merging the manually and automatically transcribed data, we learn bilingual acoustic models and run ASR experiments on the development and test data of the FAME! speech corpus to quantify the quality of the automatic transcriptions. Using these acoustic models, we present speech recognition and CS detection accuracies. The results demonstrate that applying language diarization to the raw speech data to enable using the monolingual resources improves the automatic transcription quality compared to a baseline system using a bilingual ASR system.