2023-04-21 更新
Dynamic Chuck Convolution For Unified Streaming And Non-streaming Conformer ASR
Authors:Xilai Li, Goeric Huybrechts, Srikanth Ronanki, Jeff Farris, Sravan Bodapati
Recently, there has been an increasing interest in unifying streaming and non-streaming speech recognition models to reduce development, training and deployment cost. The best-known approaches rely on either window-based or dynamic chunk-based attention strategy and causal convolutions to minimize the degradation due to streaming. However, the performance gap still remains relatively large between non-streaming and a full-contextual model trained independently. To address this, we propose a dynamic chunk-based convolution replacing the causal convolution in a hybrid Connectionist Temporal Classification (CTC)-Attention Conformer architecture. Additionally, we demonstrate further improvements through initialization of weights from a full-contextual model and parallelization of the convolution and self-attention modules. We evaluate our models on the open-source Voxpopuli, LibriSpeech and in-house conversational datasets. Overall, our proposed model reduces the degradation of the streaming mode over the non-streaming full-contextual model from 41.7% and 45.7% to 16.7% and 26.2% on the LibriSpeech test-clean and test-other datasets respectively, while improving by a relative 15.5% WER over the previous state-of-the-art unified model.
PDF 5 pages, 3 figures, 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023)
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Towards the Universal Defense for Query-Based Audio Adversarial Attacks
Authors:Feng Guo, Zheng Sun, Yuxuan Chen, Lei Ju
Recently, studies show that deep learning-based automatic speech recognition (ASR) systems are vulnerable to adversarial examples (AEs), which add a small amount of noise to the original audio examples. These AE attacks pose new challenges to deep learning security and have raised significant concerns about deploying ASR systems and devices. The existing defense methods are either limited in application or only defend on results, but not on process. In this work, we propose a novel method to infer the adversary intent and discover audio adversarial examples based on the AEs generation process. The insight of this method is based on the observation: many existing audio AE attacks utilize query-based methods, which means the adversary must send continuous and similar queries to target ASR models during the audio AE generation process. Inspired by this observation, We propose a memory mechanism by adopting audio fingerprint technology to analyze the similarity of the current query with a certain length of memory query. Thus, we can identify when a sequence of queries appears to be suspectable to generate audio AEs. Through extensive evaluation on four state-of-the-art audio AE attacks, we demonstrate that on average our defense identify the adversary intent with over 90% accuracy. With careful regard for robustness evaluations, we also analyze our proposed defense and its strength to withstand two adaptive attacks. Finally, our scheme is available out-of-the-box and directly compatible with any ensemble of ASR defense models to uncover audio AE attacks effectively without model retraining.
PDF Submitted to Cybersecurity journal
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Indian Sign Language Recognition Using Mediapipe Holistic
Authors:Dr. Velmathi G, Kaushal Goyal
Deaf individuals confront significant communication obstacles on a daily basis. Their inability to hear makes it difficult for them to communicate with those who do not understand sign language. Moreover, it presents difficulties in educational, occupational, and social contexts. By providing alternative communication channels, technology can play a crucial role in overcoming these obstacles. One such technology that can facilitate communication between deaf and hearing individuals is sign language recognition. We will create a robust system for sign language recognition in order to convert Indian Sign Language to text or speech. We will evaluate the proposed system and compare CNN and LSTM models. Since there are both static and gesture sign languages, a robust model is required to distinguish between them. In this study, we discovered that a CNN model captures letters and characters for recognition of static sign language better than an LSTM model, but it outperforms CNN by monitoring hands, faces, and pose in gesture sign language phrases and sentences. The creation of a text-to-sign language paradigm is essential since it will enhance the sign language-dependent deaf and hard-of-hearing population’s communication skills. Even though the sign-to-text translation is just one side of communication, not all deaf or hard-of-hearing people are proficient in reading or writing text. Some may have difficulty comprehending written language due to educational or literacy issues. Therefore, a text-to-sign language paradigm would allow them to comprehend text-based information and participate in a variety of social, educational, and professional settings. Keywords: deaf and hard-of-hearing, DHH, Indian sign language, CNN, LSTM, static and gesture sign languages, text-to-sign language model, MediaPipe Holistic, sign language recognition, SLR, SLT
PDF 16 pages, 22 figures
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