Leveraging native language information for improved accented speech recognition

Recognition of accented speech is a long-standing challenge for automatic speech recognition (ASR) systems, given the increasing worldwide population of bi-lingual speakers with English as their second language. If we consider foreign-accented speech as an interpolation of the native language (L1) and English (L2), using a model that can simultaneously address both languages would perform better at the acoustic level for accented speech. In this study, we explore how an end-to-end recurrent neural network (RNN) trained system with English and native languages (Spanish and Indian languages) could leverage data of native languages to improve performance for accented English speech. To this end, we examine pre-training with native languages, as well as multi-task learning (MTL) in which the main task is trained with native English and the secondary task is trained with Spanish or Indian Languages. We show that the proposed MTL model performs better than the pre-training approach and outperforms a baseline model trained simply with English data. We suggest a new setting for MTL in which the secondary task is trained with both English and the native language, using the same output set. This proposed scenario yields better performance with +11.95% and +17.55% character error rate gains over baseline for Hispanic and Indian accents, respectively.Comment: Accepted at Interspeech 201

A Speaker Diarization System for Studying Peer-Led Team Learning Groups

Peer-led team learning (PLTL) is a model for teaching STEM courses where small student groups meet periodically to collaboratively discuss coursework. Automatic analysis of PLTL sessions would help education researchers to get insight into how learning outcomes are impacted by individual participation, group behavior, team dynamics, etc.. Towards this, speech and language technology can help, and speaker diarization technology will lay the foundation for analysis. In this study, a new corpus is established called CRSS-PLTL, that contains speech data from 5 PLTL teams over a semester (10 sessions per team with 5-to-8 participants in each team). In CRSS-PLTL, every participant wears a LENA device (portable audio recorder) that provides multiple audio recordings of the event. Our proposed solution is unsupervised and contains a new online speaker change detection algorithm, termed G 3 algorithm in conjunction with Hausdorff-distance based clustering to provide improved detection accuracy. Additionally, we also exploit cross channel information to refine our diarization hypothesis. The proposed system provides good improvements in diarization error rate (DER) over the baseline LIUM system. We also present higher level analysis such as the number of conversational turns taken in a session, and speaking-time duration (participation) for each speaker.Comment: 5 Pages, 2 Figures, 2 Tables, Proceedings of INTERSPEECH 2016, San Francisco, US

Cool Customers in the Stellar Graveyard IV: Spitzer Search for Mid-IR excesses Around Five DAs

Hydrogen atmosphere white dwarfs with metal lines, so-called DAZs, require external accretion of material to explain the presence of weak metal line absorption in their photospheres. The source of this material is currently unknown, but could come from the interstellar medium, unseen companions, or relic planetesimals from asteroid belt or Kuiper belt analogues. Accurate mid-infrared photometry of these white dwarfs provide additional information to solve the mystery of this accretion and to look for evidence of planetary systems that have survived post main sequence evolution. We present {\em Spitzer} IRAC photometry accurate to $\sim$3% for four DAZs and one DA with circumstellar absorption lines in the UV. We search for excesses due to unseen companions or circumstellar dust disks. We use {\em Hubble Space Telescope} NICMOS imaging of these white dwarfs to gauge the level of background contamination to our targets as well as rule out common proper motion companions to WD 1620-391. All of our targets show no excesses due to companions $>$20 M$_{J}$, ruling out all but very low mass companions to these white dwarfs at all separations. No excesses due to circumstellar disks are observed, and we place limits on what types of disks may still be present.Comment: 18 pages, 8 figures, Accepted to A

Speaker Representation Learning using Global Context Guided Channel and Time-Frequency Transformations

In this study, we propose the global context guided channel and time-frequency transformations to model the long-range, non-local time-frequency dependencies and channel variances in speaker representations. We use the global context information to enhance important channels and recalibrate salient time-frequency locations by computing the similarity between the global context and local features. The proposed modules, together with a popular ResNet based model, are evaluated on the VoxCeleb1 dataset, which is a large scale speaker verification corpus collected in the wild. This lightweight block can be easily incorporated into a CNN model with little additional computational costs and effectively improves the speaker verification performance compared to the baseline ResNet-LDE model and the Squeeze&Excitation block by a large margin. Detailed ablation studies are also performed to analyze various factors that may impact the performance of the proposed modules. We find that by employing the proposed L2-tf-GTFC transformation block, the Equal Error Rate decreases from 4.56% to 3.07%, a relative 32.68% reduction, and a relative 27.28% improvement in terms of the DCF score. The results indicate that our proposed global context guided transformation modules can efficiently improve the learned speaker representations by achieving time-frequency and channel-wise feature recalibration.Comment: Accepted to Interspeech 202

Complex-Valued Time-Frequency Self-Attention for Speech Dereverberation

Several speech processing systems have demonstrated considerable performance improvements when deep complex neural networks (DCNN) are coupled with self-attention (SA) networks. However, the majority of DCNN-based studies on speech dereverberation that employ self-attention do not explicitly account for the inter-dependencies between real and imaginary features when computing attention. In this study, we propose a complex-valued T-F attention (TFA) module that models spectral and temporal dependencies by computing two-dimensional attention maps across time and frequency dimensions. We validate the effectiveness of our proposed complex-valued TFA module with the deep complex convolutional recurrent network (DCCRN) using the REVERB challenge corpus. Experimental findings indicate that integrating our complex-TFA module with DCCRN improves overall speech quality and performance of back-end speech applications, such as automatic speech recognition, compared to earlier approaches for self-attention.Comment: Interspeech 2022: ISCA Best Student Paper Award Finalis

MixRep: Hidden Representation Mixup for Low-Resource Speech Recognition

In this paper, we present MixRep, a simple and effective data augmentation strategy based on mixup for low-resource ASR. MixRep interpolates the feature dimensions of hidden representations in the neural network that can be applied to both the acoustic feature input and the output of each layer, which generalizes the previous MixSpeech method. Further, we propose to combine the mixup with a regularization along the time axis of the input, which is shown as complementary. We apply MixRep to a Conformer encoder of an E2E LAS architecture trained with a joint CTC loss. We experiment on the WSJ dataset and subsets of the SWB dataset, covering reading and telephony conversational speech. Experimental results show that MixRep consistently outperforms other regularization methods for low-resource ASR. Compared to a strong SpecAugment baseline, MixRep achieves a +6.5\% and a +6.7\% relative WER reduction on the eval92 set and the Callhome part of the eval'2000 set.Comment: Accepted to Interspeech 202

Data-driven Attention and Data-independent DCT based Global Context Modeling for Text-independent Speaker Recognition

Learning an effective speaker representation is crucial for achieving reliable performance in speaker verification tasks. Speech signals are high-dimensional, long, and variable-length sequences that entail a complex hierarchical structure. Signals may contain diverse information at each time-frequency (TF) location. For example, it may be more beneficial to focus on high-energy parts for phoneme classes such as fricatives. The standard convolutional layer that operates on neighboring local regions cannot capture the complex TF global context information. In this study, a general global time-frequency context modeling framework is proposed to leverage the context information specifically for speaker representation modeling. First, a data-driven attention-based context model is introduced to capture the long-range and non-local relationship across different time-frequency locations. Second, a data-independent 2D-DCT based context model is proposed to improve model interpretability. A multi-DCT attention mechanism is presented to improve modeling power with alternate DCT base forms. Finally, the global context information is used to recalibrate salient time-frequency locations by computing the similarity between the global context and local features. The proposed lightweight blocks can be easily incorporated into a speaker model with little additional computational costs and effectively improves the speaker verification performance compared to the standard ResNet model and Squeeze\&Excitation block by a large margin. Detailed ablation studies are also performed to analyze various factors that may impact performance of the proposed individual modules. Results from experiments show that the proposed global context modeling framework can efficiently improve the learned speaker representations by achieving channel-wise and time-frequency feature recalibration

Fearless Steps Challenge Phase-1 Evaluation Plan

The Fearless Steps Challenge 2019 Phase-1 (FSC-P1) is the inaugural Challenge of the Fearless Steps Initiative hosted by the Center for Robust Speech Systems (CRSS) at the University of Texas at Dallas. The goal of this Challenge is to evaluate the performance of state-of-the-art speech and language systems for large task-oriented teams with naturalistic audio in challenging environments. Researchers may select to participate in any single or multiple of these challenge tasks. Researchers may also choose to employ the FEARLESS STEPS corpus for other related speech applications. All participants are encouraged to submit their solutions and results for consideration in the ISCA INTERSPEECH-2019 special session.Comment: Document Generated in February 2019 for conducting the Fearless Steps Challenge Phase-1 and its associated ISCA Interspeech-2019 Special Sessio