pwc-archive/papers-with-abstracts huggingface dataset:
Comparing in context: Improving cosine similarity measures with a metric tensor
Authors: Isa M. Apallius de Vos, Ghislaine L. van den Boogerd, Mara D. Fennema, Adriana D. Correia
Date: 2022-03-28 00:00:00
Proceeding: ICON 2021 12
Tasks: Language Modeling, Language Modelling, Word Embeddings, Word Similarity

Cosine similarity is a widely used measure of the relatedness of pre-trained word embeddings, trained on a language modeling goal. Datasets such as WordSim-353 and SimLex-999 rate how similar words are according to human annotators, and as such are often used to evaluate the performance of language models. Thus, any improvement on the word similarity task requires an improved word representation. In this paper, we propose instead the use of an extended cosine similarity measure to improve performance on that task, with gains in interpretability. We explore the hypothesis that this approach is particularly useful if the word-similarity pairs share the same context, for which distinct contextualized similarity measures can be learned. We first use the dataset of Richie et al. (2020) to learn contextualized metrics and compare the results with the baseline values obtained using the standard cosine similarity measure, which consistently shows improvement. We also train a contextualized similarity measure for both SimLex-999 and WordSim-353, comparing the results with the corresponding baselines, and using these datasets as independent test sets for the all-context similarity measure learned on the contextualized dataset, obtaining positive results for a number of tests.
Convex Non-negative Matrix Factorization Through Quantum Annealing
Authors: Ahmed Zaiou, Basarab Matei, Younès Bennani, Mohamed Hibti
Date: 2022-03-28 00:00:00

In this paper we provide the quantum version of the Convex Non-negative Matrix Factorization algorithm (Convex-NMF) by using the D-wave quantum annealer. More precisely, we use D-wave 2000Q to find the low rank approximation of a fixed real-valued matrix X by the product of two non-negative matrices factors W and G such that the Frobenius norm of the difference X-XWG is minimized. In order to solve this optimization problem we proceed in two steps. In the first step we transform the global real optimization problem depending on W,G into two quadratic unconstrained binary optimization problems (QUBO) depending on W and G respectively. In the second step we use an alternative strategy between the two QUBO problems corresponding to W and G to find the global solution. The running of these two QUBO problems on D-wave 2000Q need to use an embedding to the chimera graph of D-wave 2000Q, this embedding is limited by the number of qubits of D-wave 2000Q. We perform a study on the maximum number of real data to be used by our approach on D-wave 2000Q. The proposed study is based on the number of qubits used to represent each real variable. We also tested our approach on D-Wave 2000Q with several randomly generated data sets to prove that our approach is faster than the classical approach and also to prove that it gets the best results.
Attacker Attribution of Audio Deepfakes
Authors: Nicolas M. Müller, Franziska Dieckmann, Jennifer Williams
Date: 2022-03-28 00:00:00
Tasks: DeepFake Detection, Face Swapping, Misinformation

Deepfakes are synthetically generated media often devised with malicious intent. They have become increasingly more convincing with large training datasets advanced neural networks. These fakes are readily being misused for slander, misinformation and fraud. For this reason, intensive research for developing countermeasures is also expanding. However, recent work is almost exclusively limited to deepfake detection - predicting if audio is real or fake. This is despite the fact that attribution (who created which fake?) is an essential building block of a larger defense strategy, as practiced in the field of cybersecurity for a long time. This paper considers the problem of deepfake attacker attribution in the domain of audio. We present several methods for creating attacker signatures using low-level acoustic descriptors and machine learning embeddings. We show that speech signal features are inadequate for characterizing attacker signatures. However, we also demonstrate that embeddings from a recurrent neural network can successfully characterize attacks from both known and unknown attackers. Our attack signature embeddings result in distinct clusters, both for seen and unseen audio deepfakes. We show that these embeddings can be used in downstream-tasks to high-effect, scoring 97.10% accuracy in attacker-id classification.
Subspace-based Representation and Learning for Phonotactic Spoken Language Recognition
Authors: Hung-Shin Lee, Yu Tsao, Shyh-Kang Jeng, Hsin-Min Wang
Date: 2022-03-28 00:00:00

Phonotactic constraints can be employed to distinguish languages by representing a speech utterance as a multinomial distribution or phone events. In the present study, we propose a new learning mechanism based on subspace-based representation, which can extract concealed phonotactic structures from utterances, for language verification and dialect/accent identification. The framework mainly involves two successive parts. The first part involves subspace construction. Specifically, it decodes each utterance into a sequence of vectors filled with phone-posteriors and transforms the vector sequence into a linear orthogonal subspace based on low-rank matrix factorization or dynamic linear modeling. The second part involves subspace learning based on kernel machines, such as support vector machines and the newly developed subspace-based neural networks (SNNs). The input layer of SNNs is specifically designed for the sample represented by subspaces. The topology ensures that the same output can be derived from identical subspaces by modifying the conventional feed-forward pass to fit the mathematical definition of subspace similarity. Evaluated on the "General LR" test of NIST LRE 2007, the proposed method achieved up to 52%, 46%, 56%, and 27% relative reductions in equal error rates over the sequence-based PPR-LM, PPR-VSM, and PPR-IVEC methods and the lattice-based PPR-LM method, respectively. Furthermore, on the dialect/accent identification task of NIST LRE 2009, the SNN-based system performed better than the aforementioned four baseline methods.
A Dataset for Speech Emotion Recognition in Greek Theatrical Plays
Authors: Maria Moutti, Sofia Eleftheriou, Panagiotis Koromilas, Theodoros Giannakopoulos
Date: 2022-03-27 00:00:00
Proceeding: LREC 2022 6
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Emotion Recognition, Speech Emotion Recognition, speech-recognition, Speech Recognition, Speech-to-Text

Machine learning methodologies can be adopted in cultural applications and propose new ways to distribute or even present the cultural content to the public. For instance, speech analytics can be adopted to automatically generate subtitles in theatrical plays, in order to (among other purposes) help people with hearing loss. Apart from a typical speech-to-text transcription with Automatic Speech Recognition (ASR), Speech Emotion Recognition (SER) can be used to automatically predict the underlying emotional content of speech dialogues in theatrical plays, and thus to provide a deeper understanding how the actors utter their lines. However, real-world datasets from theatrical plays are not available in the literature. In this work we present GreThE, the Greek Theatrical Emotion dataset, a new publicly available data collection for speech emotion recognition in Greek theatrical plays. The dataset contains utterances from various actors and plays, along with respective valence and arousal annotations. Towards this end, multiple annotators have been asked to provide their input for each speech recording and inter-annotator agreement is taken into account in the final ground truth generation. In addition, we discuss the results of some indicative experiments that have been conducted with machine and deep learning frameworks, using the dataset, along with some widely used databases in the field of speech emotion recognition.
Streaming parallel transducer beam search with fast-slow cascaded encoders
Authors: Jay Mahadeokar, Yangyang Shi, Ke Li, Duc Le, Jiedan Zhu, Vikas Chandra, Ozlem Kalinli, Michael L Seltzer
Date: 2022-03-29 00:00:00
Tasks: Low-latency processing, speech-recognition, Speech Recognition

Streaming ASR with strict latency constraints is required in many speech recognition applications. In order to achieve the required latency, streaming ASR models sacrifice accuracy compared to non-streaming ASR models due to lack of future input context. Previous research has shown that streaming and non-streaming ASR for RNN Transducers can be unified by cascading causal and non-causal encoders. This work improves upon this cascaded encoders framework by leveraging two streaming non-causal encoders with variable input context sizes that can produce outputs at different audio intervals (e.g. fast and slow). We propose a novel parallel time-synchronous beam search algorithm for transducers that decodes from fast-slow encoders, where the slow encoder corrects the mistakes generated from the fast encoder. The proposed algorithm, achieves up to 20% WER reduction with a slight increase in token emission delays on the public Librispeech dataset and in-house datasets. We also explore techniques to reduce the computation by distributing processing between the fast and slow encoders. Lastly, we explore sharing the parameters in the fast encoder to reduce the memory footprint. This enables low latency processing on edge devices with low computation cost and a low memory footprint.
Integrating Lattice-Free MMI into End-to-End Speech Recognition
Authors: Jinchuan Tian, Jianwei Yu, Chao Weng, Yuexian Zou, Dong Yu
Date: 2022-03-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In automatic speech recognition (ASR) research, discriminative criteria have achieved superior performance in DNN-HMM systems. Given this success, the adoption of discriminative criteria is promising to boost the performance of end-to-end (E2E) ASR systems. With this motivation, previous works have introduced the minimum Bayesian risk (MBR, one of the discriminative criteria) into E2E ASR systems. However, the effectiveness and efficiency of the MBR-based methods are compromised: the MBR criterion is only used in system training, which creates a mismatch between training and decoding; the on-the-fly decoding process in MBR-based methods results in the need for pre-trained models and slow training speeds. To this end, novel algorithms are proposed in this work to integrate another widely used discriminative criterion, lattice-free maximum mutual information (LF-MMI), into E2E ASR systems not only in the training stage but also in the decoding process. The proposed LF-MMI training and decoding methods show their effectiveness on two widely used E2E frameworks: Attention-Based Encoder-Decoders (AEDs) and Neural Transducers (NTs). Compared with MBR-based methods, the proposed LF-MMI method: maintains the consistency between training and decoding; eschews the on-the-fly decoding process; trains from randomly initialized models with superior training efficiency. Experiments suggest that the LF-MMI method outperforms its MBR counterparts and consistently leads to statistically significant performance improvements on various frameworks and datasets from 30 hours to 14.3k hours. The proposed method achieves state-of-the-art (SOTA) results on Aishell-1 (CER 4.10%) and Aishell-2 (CER 5.02%) datasets. Code is released.
Dynamic Latency for CTC-Based Streaming Automatic Speech Recognition With Emformer
Authors: Jingyu Sun, Guiping Zhong, Dinghao Zhou, Baoxiang Li
Date: 2022-03-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

An inferior performance of the streaming automatic speech recognition models versus non-streaming model is frequently seen due to the absence of future context. In order to improve the performance of the streaming model and reduce the computational complexity, a frame-level model using efficient augment memory transformer block and dynamic latency training method is employed for streaming automatic speech recognition in this paper. The long-range history context is stored into the augment memory bank as a complement to the limited history context used in the encoder. Key and value are cached by a cache mechanism and reused for next chunk to reduce computation. Afterwards, a dynamic latency training method is proposed to obtain better performance and support low and high latency inference simultaneously. Our experiments are conducted on benchmark 960h LibriSpeech data set. With an average latency of 640ms, our model achieves a relative WER reduction of 6.0% on test-clean and 3.0% on test-other versus the truncate chunk-wise Transformer.
Earnings-22: A Practical Benchmark for Accents in the Wild
Authors: Miguel Del Rio, Peter Ha, Quinten McNamara, Corey Miller, Shipra Chandra
Date: 2022-03-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Benchmarking, speech-recognition, Speech Recognition

Modern automatic speech recognition (ASR) systems have achieved superhuman Word Error Rate (WER) on many common corpora despite lacking adequate performance on speech in the wild. Beyond that, there is a lack of real-world, accented corpora to properly benchmark academic and commercial models. To ensure this type of speech is represented in ASR benchmarking, we present Earnings-22, a 125 file, 119 hour corpus of English-language earnings calls gathered from global companies. We run a comparison across 4 commercial models showing the variation in performance when taking country of origin into consideration. Looking at hypothesis transcriptions, we explore errors common to all ASR systems tested. By examining Individual Word Error Rate (IWER), we find that key speech features impact model performance more for certain accents than others. Earnings-22 provides a free-to-use benchmark of real-world, accented audio to bridge academic and industrial research.
Heuristic-based Inter-training to Improve Few-shot Multi-perspective Dialog Summarization
Authors: Benjamin Sznajder, Chulaka Gunasekara, Guy Lev, Sachin Joshi, Eyal Shnarch, Noam Slonim
Date: 2022-03-29 00:00:00
Tasks: Decision Making

Many organizations require their customer-care agents to manually summarize their conversations with customers. These summaries are vital for decision making purposes of the organizations. The perspective of the summary that is required to be created depends on the application of the summaries. With this work, we study the multi-perspective summarization of customer-care conversations between support agents and customers. We observe that there are different heuristics that are associated with summaries of different perspectives, and explore these heuristics to create weak-labeled data for intermediate training of the models before fine-tuning with scarce human annotated summaries. Most importantly, we show that our approach supports models to generate multi-perspective summaries with a very small amount of annotated data. For example, our approach achieves 94\% of the performance (Rouge-2) of a model trained with the original data, by training only with 7\% of the original data.
Interactive Audio-text Representation for Automated Audio Captioning with Contrastive Learning
Authors: Chen Chen, Nana Hou, Yuchen Hu, Heqing Zou, Xiaofeng Qi, Eng Siong Chng
Date: 2022-03-29 00:00:00
Tasks: Audio captioning, Contrastive Learning

Automated Audio captioning (AAC) is a cross-modal task that generates natural language to describe the content of input audio. Most prior works usually extract single-modality acoustic features and are therefore sub-optimal for the cross-modal decoding task. In this work, we propose a novel AAC system called CLIP-AAC to learn interactive cross-modality representation with both acoustic and textual information. Specifically, the proposed CLIP-AAC introduces an audio-head and a text-head in the pre-trained encoder to extract audio-text information. Furthermore, we also apply contrastive learning to narrow the domain difference by learning the correspondence between the audio signal and its paired captions. Experimental results show that the proposed CLIP-AAC approach surpasses the best baseline by a significant margin on the Clotho dataset in terms of NLP evaluation metrics. The ablation study indicates that both the pre-trained model and contrastive learning contribute to the performance gain of the AAC model.
Representing 'how you say' with 'what you say': English corpus of focused speech and text reflecting corresponding implications
Authors: Naoaki Suzuki, Satoshi Nakamura
Date: 2022-03-29 00:00:00
Tasks: Sentence, Translation

In speech communication, how something is said (paralinguistic information) is as crucial as what is said (linguistic information). As a type of paralinguistic information, English speech uses sentence stress, the heaviest prominence within a sentence, to convey emphasis. While different placements of sentence stress communicate different emphatic implications, current speech translation systems return the same translations if the utterances are linguistically identical, losing paralinguistic information. Concentrating on focus, a type of emphasis, we propose mapping paralinguistic information into the linguistic domain within the source language using lexical and grammatical devices. This method enables us to translate the paraphrased text representations instead of the transcription of the original speech and obtain translations that preserve paralinguistic information. As a first step, we present the collection of an English corpus containing speech that differed in the placement of focus along with the corresponding text, which was designed to reflect the implied meaning of the speech. Also, analyses of our corpus demonstrated that mapping of focus from the paralinguistic domain into the linguistic domain involved various lexical and grammatical methods. The data and insights from our analysis will further advance research into paralinguistic translation. The corpus will be published via LDC and our website.
Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation
Authors: Ryo Fukuda, Katsuhito Sudoh, Satoshi Nakamura
Date: 2022-03-29 00:00:00
Tasks: Binary Classification, Segmentation, Sentence, Translation

Speech segmentation, which splits long speech into short segments, is essential for speech translation (ST). Popular VAD tools like WebRTC VAD have generally relied on pause-based segmentation. Unfortunately, pauses in speech do not necessarily match sentence boundaries, and sentences can be connected by a very short pause that is difficult to detect by VAD. In this study, we propose a speech segmentation method using a binary classification model trained using a segmented bilingual speech corpus. We also propose a hybrid method that combines VAD and the above speech segmentation method. Experimental results revealed that the proposed method is more suitable for cascade and end-to-end ST systems than conventional segmentation methods. The hybrid approach further improved the translation performance.
WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit
Authors: BinBin Zhang, Di wu, Zhendong Peng, Xingchen Song, Zhuoyuan Yao, Hang Lv, Lei Xie, Chao Yang, Fuping Pan, Jianwei Niu
Date: 2022-03-29 00:00:00
Tasks: Decoder, Language Modelling, speech-recognition, Speech Recognition

Recently, we made available WeNet, a production-oriented end-to-end speech recognition toolkit, which introduces a unified two-pass (U2) framework and a built-in runtime to address the streaming and non-streaming decoding modes in a single model. To further improve ASR performance and facilitate various production requirements, in this paper, we present WeNet 2.0 with four important updates. (1) We propose U2++, a unified two-pass framework with bidirectional attention decoders, which includes the future contextual information by a right-to-left attention decoder to improve the representative ability of the shared encoder and the performance during the rescoring stage. (2) We introduce an n-gram based language model and a WFST-based decoder into WeNet 2.0, promoting the use of rich text data in production scenarios. (3) We design a unified contextual biasing framework, which leverages user-specific context (e.g., contact lists) to provide rapid adaptation ability for production and improves ASR accuracy in both with-LM and without-LM scenarios. (4) We design a unified IO to support large-scale data for effective model training. In summary, the brand-new WeNet 2.0 achieves up to 10\% relative recognition performance improvement over the original WeNet on various corpora and makes available several important production-oriented features.
Semantic properties of English nominal pluralization: Insights from word embeddings
Authors: Elnaz Shafaei-Bajestan, Masoumeh Moradipour-Tari, Peter Uhrig, R. Harald Baayen
Date: 2022-03-29 00:00:00
Tasks: Word Embeddings

Semantic differentiation of nominal pluralization is grammaticalized in many languages. For example, plural markers may only be relevant for human nouns. English does not appear to make such distinctions. Using distributional semantics, we show that English nominal pluralization exhibits semantic clusters. For instance, pluralization of fruit words is more similar to one another and less similar to pluralization of other semantic classes. Therefore, reduction of the meaning shift in plural formation to the addition of an abstract plural meaning is too simplistic. A semantically informed method, called CosClassAvg, is introduced that outperforms pluralization methods in distributional semantics which assume plural formation amounts to the addition of a fixed plural vector. In comparison with our approach, a method from compositional distributional semantics, called FRACSS, predicted plural vectors that were more similar to the corpus-extracted plural vectors in terms of direction but not vector length. A modeling study reveals that the observed difference between the two predicted semantic spaces by CosClassAvg and FRACSS carries over to how well a computational model of the listener can understand previously unencountered plural forms. Mappings from word forms, represented with triphone vectors, to predicted semantic vectors are more productive when CosClassAvg-generated semantic vectors are employed as gold standard vectors instead of FRACSS-generated vectors.
Short-Term Word-Learning in a Dynamically Changing Environment
Authors: Christian Huber, Rishu Kumar, Ondřej Bojar, Alexander Waibel
Date: 2022-03-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Neural sequence-to-sequence automatic speech recognition (ASR) systems are in principle open vocabulary systems, when using appropriate modeling units. In practice, however, they often fail to recognize words not seen during training, e.g., named entities, numbers or technical terms. To alleviate this problem, Huber et al. proposed to supplement an end-to-end ASR system with a word/phrase memory and a mechanism to access this memory to recognize the words and phrases correctly. In this paper we study, a) methods to acquire important words for this memory dynamically and, b) the trade-off between improvement in recognition accuracy of new words and the potential danger of false alarms for those added words. We demonstrate significant improvements in the detection rate of new words with only a minor increase in false alarms (F1 score 0.30 $\rightarrow$ 0.80), when using an appropriate number of new words. In addition, we show that important keywords can be extracted from supporting documents and used effectively.