Language Identification Using Spectral and Prosodic Features for Speaker Verification

Language identification is the task of determining the language of a spoken utterance. This is a challenging task, as there is a great deal of variation in the way that different languages are spoken, and even within the same language, there can be a great deal of variation in the way that different speakers pronounce words and phrases. Despite these challenges, language identification is an important task, as it can be used for a variety of applications, such as speaker verification, machine translation, and language learning.

Language Identification Using Spectral and Prosodic Features (SpringerBriefs in Speech Technology)

by Mangey Ram

4.5 out of 5

Language	:	English
File size	:	3614 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	154 pages
Paperback	:	236 pages
Item Weight	:	12 ounces
Dimensions	:	6.14 x 0.5 x 9.21 inches

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In this book, we present a comprehensive study of language identification using spectral and prosodic features for speaker verification. We provide a detailed overview of the field, including the different approaches that have been used to identify languages from speech, as well as the challenges involved in this task. We also present a novel approach to language identification that utilizes both spectral and prosodic features, and we demonstrate the effectiveness of this approach on a variety of real-world datasets.

Background

The field of language identification has a long history, with the first attempts to identify languages from speech dating back to the early 20th century. However, it was not until the advent of digital speech processing in the 1980s that language identification became a practical reality. Since then, there has been a great deal of research in this area, and a variety of different approaches to language identification have been developed.

One of the most successful approaches to language identification is the use of spectral features. Spectral features are derived from the frequency spectrum of speech, and they can be used to characterize the formants of the speech signal. Formants are the resonant frequencies of the vocal tract, and they play an important role in determining the sound of a language. By measuring the formants of a speech signal, it is possible to identify the language of the speaker.

Another successful approach to language identification is the use of prosodic features. Prosodic features are related to the intonation and rhythm of speech, and they can also be used to characterize the language of the speaker. By measuring the prosodic features of a speech signal, it is possible to identify the language of the speaker.

Our Approach

In this book, we present a novel approach to language identification that utilizes both spectral and prosodic features. Our approach is based on the hypothesis that spectral and prosodic features are complementary, and that by combining these two types of features, we can achieve better language identification accuracy than by using either type of feature alone.

Our approach consists of two main steps. In the first step, we extract spectral and prosodic features from the speech signal. In the second step, we use a machine learning algorithm to classify the speech signal into one of several languages.

We have evaluated our approach on a variety of real-world datasets, and we have found that it achieves state-of-the-art language identification accuracy. Our approach is also computationally efficient, making it suitable for real-time applications.

Applications

Language identification has a wide range of applications, including:

• Speaker verification: Language identification can be used to verify the identity of a speaker by comparing the language of their speech to the language of their known recordings.
• Machine translation: Language identification can be used to automatically translate speech from one language to another.
• Language learning: Language identification can be used to help people learn new languages by providing them with feedback on the pronunciation of their speech.

In this book, we have presented a comprehensive study of language identification using spectral and prosodic features for speaker verification. We have provided a detailed overview of the field, including the different approaches that have been used to identify languages from speech, as well as the challenges involved in this task. We have also presented a novel approach to language identification that utilizes both spectral and prosodic features, and we have demonstrated the effectiveness of this approach on a variety of real-world datasets. This book is a valuable resource for researchers and practitioners who are interested in language identification, speaker verification, or speech processing.

References

1. D. A. Reynolds and R. C. Rose, "Robust text-independent speaker identification using Gaussian mixture models," IEEE Transactions on Speech and Audio Processing, vol. 13, no. 4, pp. 726-733, 2005.
2. L. R. Rabiner and B

Language Identification Using Spectral and Prosodic Features (SpringerBriefs in Speech Technology)

by Mangey Ram

4.5 out of 5

Language	:	English
File size	:	3614 KB
Text-to-Speech	:	Enabled
Screen Reader	:	Supported
Enhanced typesetting	:	Enabled
Print length	:	154 pages
Paperback	:	236 pages
Item Weight	:	12 ounces
Dimensions	:	6.14 x 0.5 x 9.21 inches

FREE