Bidirectional Attention for Text-Dependent Speaker Verification
نویسندگان
چکیده
منابع مشابه
Attention-Based Models for Text-Dependent Speaker Verification
Attention-based models have recently shown great performance on a range of tasks, such as speech recognition, machine translation, and image captioning due to their ability to summarize relevant information that expands through the entire length of an input sequence. In this paper, we analyze the usage of attention mechanisms to the problem of sequence summarization in our end-to-end textdepend...
متن کاملDeep feature for text-dependent speaker verification
Recently deep learning has been successfully used in speech recognition, however it has not been carefully explored and widely accepted for speaker verification. To incorporate deep learning into speaker verification, this paper proposes novel approaches of extracting and using features from deep learning models for text-dependent speaker verification. In contrast to the traditional short-term ...
متن کاملDomain adaptation for text dependent speaker verification
Recently we have investigated the use of state-of-the-art textdependent speaker verification algorithms for user authentication and obtained satisfactory results mainly by using a fair amount of text-dependent development data from the target domain. In this work we investigate the ability to build high accuracy text-dependent systems using no data at all from the target domain. Instead of usin...
متن کاملTemplate-matching for text-dependent speaker verification
In the last decade, i-vector and Joint Factor Analysis (JFA) approaches to speaker modeling have become ubiquitous in the area of automatic speaker recognition. Both of these techniques involve the computation of posterior probabilities, using either Gaussian Mixture Models (GMM) or Deep Neural Networks (DNN), as a prior step to estimating i-vectors or speaker factors. GMMs focus on implicitly ...
متن کاملContent Normalization for Text-Dependent Speaker Verification
Subspace based techniques, such as i-vector and Joint Factor Analysis (JFA) have shown to provide state-of-the-art performance for fixed phrase based text-dependent speaker verification. However, the error rates of such systems on the random digit task of RSR dataset are higher than that of Gaussian Mixture Model-Universal Background Model (GMM-UBM). In this paper, we aim at improving i-vector ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Sensors
سال: 2020
ISSN: 1424-8220
DOI: 10.3390/s20236784