attention mechanism in speaker recognition what does it learn in deep speaker embedding

Abstract: This paper presents an experimental study on deep speaker embedding with an attention mechanism that has been found to be a powerful representation learning technique in speaker recognition. In this framework, an attention model works as a frame

Author: Qiongqiong Wang, Koji Okabe, Kong Aik Lee, Hitoshi Yamamoto, Takafumi Koshinaka
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2.2. Self-attentive speaker embeddings Self-attention mechanism can be effectively used to encode a variable-length sequence into some ﬁxed-length embeddings. Inspired by the structured self-attention mechanism proposed in [18] for sentence embedding, we

5/10/2019 · This paper investigates replacing i-vectors for text-independent speaker verification with embeddings extracted from a feedforward deep neural network. Long-term speaker characteristics are captured in the network by a temporal pooling layer that aggregates over the

ATTENTION MECHANISM IN SPEAKER RECOGNITION: WHAT DOES IT LEARN IN DEEP SPEAKER EMBEDDING? Workshop on Spoken Language Technology (SLT) 2018 2018年9月3 日その他の著者 Investigation of Speaker Verification Performance Using

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In this paper, we aim to improve traditional DNN x-vector language identification performance by employing wide residual networks (WRN) as a powerful feature extractor which we

Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised.[1][2][3] Deep learning architectures such as deep neural networks, deep belief networks

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Phonetically-aware embeddings, Wide Residual Networks with Time-Delay Neural Networks and Self Attention models for the 2018 NIST Speaker Recognition Evaluation Ignacio Vinals, Dayana Ribas, Victoria Mingote, Jorge Llombart, Pablo Gimeno, Antonio Miguel,˜

30/10/2019 · Need help for retraining and cross validation and see if the ROUGE score matches exactly (or better) with the numbers reported in the paper. I just train for 500k iteration (with batch size 8) with pointer generation enabled + coverage loss disabled and next 100k

Qiongqiong Wang, Koji Okabe, Kong Aik Lee, Hitoshi Yamamoto, and Takafumi Koshinaka, “Attention Mechanism in Speaker Recognition: What Does It Learn in Deep Speaker Embedding?” The IEEE Workshop on Spoken Language Technology (SLT) 2018年12

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naive average pooling, this phonetic-attention scoring ap-proach can deliver consistent performance improvement in ASV tasks of both text-dependent and text-independent. Index Terms— speaker recognition, deep neural net-work, attention 1. INTRODUCTION

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Deep learning is a class of machine learning algorithms that (pp199–200) uses multiple layers to progressively extract higher level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the

1/11/2019 · An overview of attention mechanisms and memory in deep neural networks and why they work, including some specific applications in natural language processing and beyond. A recent trend in Deep Learning are Attention Mechanisms. In an interview, Ilya Sutskever, now the research director of

Selective attention is the process of focusing on a particular object in the environment for a certain period of time. Attention is a limited resource, so selective attention allows us to tune out unimportant details and focus on what matters. This differs from inattentional

Authors used speaker ids for each utterance in order to generate an answer, which conditioned not only on encoder state, but also on speaker embedding. Speaker embeddings are learned from scratch along with the model.

12/6/2019 · Spreaker provides you with all the tools you need to start a podcast and distribute it on Apple Podcasts, Spotify and more. Try our monetization features, analytics or enjoy our

3/10/2017 · How to learn a word embedding while fitting a neural network. How to use a pre-trained word embedding in a neural network. Discover how to develop deep learning models for text classification, translation, photo captioning and more in my new book ,

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What are the recent advances in implementing a Gated Convolutional Neural Network with Segment-Level Attention Mechanism for home activity monitoring? Detection and Classification of Acoustic Scenes and Events (DCASE) Challenge is one of the most important international challenges concerning acoustic event detection and classification.

Although the conventional deep learning-based feature extraction schemes have shown the potential of using nonlinearly extracted features in speaker recognition, they are usually trained in a supervised manner and it is almost impossible to apply them when no

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To appear in: Proc. 27th International Conference on Artificial Neural Networks “ICANN 2018” Combining Articulatory Features with End-to-end Learning in Speech Recognition Leyuan Qu, Cornelius Weber, Egor Lakomkin, Johannes Twiefel, Stefan Wermter

Two-Stage Temporal Multimodal Learning for Speaker and Speech Recognition Qianli Ma, Lifeng Shen, Ruishi Su, Jieyu Chen Pages 64-72 SLICE: Structural and Label Information Combined Embedding

Using GMM based attention model (Graved 2013) which ensures monotonic attention. Speaker embedding is used for voice adaptation. The embedding is done by a multi-class network trained on speaker identification. All networks in the architecture are .

5/6/2017 · Get corrections from Grammarly while you write on Gmail, Twitter, LinkedIn, and all your other favorite sites. From grammar and spelling to style and tone, Grammarly helps you eliminate errors and find the perfect words to express yourself. Grammarly allows me to get those communications out and

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Deep Voice 2: Multi-Speaker Neural Text-to-Speech Sercan Ö. Arık [email protected] Gregory Diamos [email protected] Andrew Gibiansky [email protected] John Miller [email protected] Kainan Peng [email protected]

27/5/2015 · Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition

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Speaker Embedding Extraction with Phonetic Information Yi Liu, Liang He, Jia Liu, Michael T. Johnson Attentive Statistics Pooling for Deep Speaker Embedding Koji Okabe, Takafumi Koshinaka, Koichi Shinoda Robust and Discriminative Speaker Embedding

Dual Attention Network for Scene Segmentation “Dual Attention Network for Scene Segmentation” improves scene segmentation tasks performance by attaching self-attention mechanism. It is in arxiv yet and the authors are from CASIA IVA.

18/12/2006 · What is cognition? Cognition has to do with how a person understands the world and acts in it. It is the set of mental abilities or processes that are part of nearly every human action while we are awake. Cognitive abilities are brain-based skills we need to carry out

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IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 24, NO. 5, MAY 2016 967 A Deep Ensemble Learning Method for Monaural Speech Separation Xiao-Lei Zhang, Member, IEEE, and DeLiang Wang, Fellow, IEEE Abstract—Monaural speech separation is a fundamental prob

Not amazing recognition quality, but dead simple setup, and it is possible to integrate a language model as well (I never needed one for my task). The author showed it as well in [1], but kind of skimmed right by – but to me if you want to know speech recognition in detail, pocketsphinx-python is

30/8/2018 · Research Guide for Transformers – Oct 30, 2019. The problem with RNNs and CNNs is that they aren’t able to keep up with context and content when sentences are too long. This limitation has been solved by paying attention to the word that is currently

In contrast, deep learning’s learned features are easy to adapt and fast to learn. Deep Learning provides a very flexible, universal, and learnable framework for representing the world, for both visual and linguistic information. Initially, it resulted in breakthroughs in.

7/2/2012 · Auditory perception and cognition entails both low-level and high-level processes, which are likely to interact with each other to create our rich conscious experience of soundscapes. Recent research that we review has revealed numerous influences of high-level factors, such as attention

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That attention mechanism emits source frame-wise weights to sum the encoded source frames X e as a target frame-wise vector to be transformed with the prefix Y 0 [0: t − 1]. We refer to this type of attention as “source attention”.

• Combining Speaker Role Recognition (SRR) with other speech processing modules (e.g. diarization, speech recognition) in order both to improve the performance of SRR itself and to provide useful information to the module it is combined with.

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Deep models are deep in precisely the sense that they learn many layers of computation. It turns out that these many-layered (or hierarchical) models are capable of addressing low-level perceptual data in a way that previous tools could not.