Speaker recognition in multi-speaker environment using support vector machine

Electronics group

Master thesis

Abstract:

Speaker identification is one of the topics discussed in speech processing. Speaker identification is the process of identifying who is really speaking and when using the speech signal. The goal is to design a system that can identify the change in the speaker and tag each speaker's speech for the system. It means to specify which speaker spoke in which intervals. Today, this practice has been popularized by a new title that encompasses both the process of separation and labeling called Speaker Diarization. The purpose of segmentation is to divide the speech signal into parts that only contain the speech of one speaker, and the purpose of clustering is to identify the speech parts of a speaker and assign a single label to them. The purpose of this thesis is to design and implement a speaker segmentation and clustering system using new algorithms and also to improve the results of these algorithms for this issue. This system must correctly recognize the change points of the speaker without knowing the previous information about the speaker and finally put all the audio parts related to a speaker in one cluster. In the first step, the non-speech parts are removed from the speech parts of the audio file, in order to increase the accuracy and speed of the system operation in the next steps. Then the speech file is divided into homogeneous parts in which there is only one speaker's speech. In the third step, using appropriate clustering, the speech parts of the previous step, which belong to a speaker, are placed in a cluster. To implement the system, four types of MFCC feature vectors root-MFCC, TDC, and root-TDC and three types of databases have been used, and the accuracy of the segmentation stage was 80%, and the accuracy of the clustering stage was 59% using the support vector machine. rtl;">speaker segmentation

audio segments recognition

speaker clustering

introduction

Today, multimedia data include a significant part of human knowledge. The amount of multimedia files archived in various institutions has increased significantly in recent years. The accessibility and clarity of these files can be of great help to people who are looking for information. Therefore, searching and retrieving information in this high volume is a task that requires a computer system. And as a result, one of the research areas that has recently received attention is related to the structuring of multimedia files. Among these data, voice information is more important. Because most of the archives contain audio data from TV and radio reports as well as phone conversations. In recent years, extensive research has been started in this field and acceptable results have been obtained. Among the other uses of this field in identifying the guilty, separating the important words of a witness or accused in the court and so on. It can be mentioned.

In the audio application, the main information in the files is the speech of a number of speakers, and the purpose of the final system is to answer the question of who spoke at what times? Different parts of this research field have different names such as: Speaker segmentation [1], speaker detection [2], robust transcription [3], and speaker indexing [4] have been called. Such systems are used for easy movement of audio data in long audio files (such as: news, meetings and meetings of a company, etc.) that belong to several speakers. Long radio conversations and calculations are environments in which several speakers are present and talk to each other. The ultimate goal of such systems is to implement appropriate methods to distribute audio files to areas where a particular speaker has spoken. Easy access to parts of a speaker's speech is provided by this system.

With the increase in the number of text documents available on the Internet, the need for techniques such as text indexing in order to facilitate access and search in these documents increased. Similar to this need, with the increase in the number of audio documents such as lectures, interviews and gatherings. was created Obviously, accessing audio documents is much more difficult than accessing text, and listening to a recorded audio file is more time-consuming than reading text, and manual indexing of audio documents is difficult compared to text indexing. The proposed solution to solve this problem is the automatic cataloging of audio documents[5]. In 2001, Pelkan and Sidharun and their group improved the results of the system by reducing the effect of noise on the signal and led to better speaker separation. In 2005, Boulian and Kenny obtained different results by using other feature vectors (or integrating previous methods) and using Gaussian models in the system. In 2005, Yamashita and Matsunaga improved the speaker segmentation results of this system by using audio signal features such as signal pitch frequency, energy, signal maximum frequencies, and three other features.[1] And in the following years, by performing different methods on its different parts, until today these systems have been completed and the results have been improved.

The purpose of this thesis is to design and implement a system that can identify the change in the speaker in an audio file that includes the speech of several speakers and, as far as possible, categorize the speech of each speaker without knowing his previous information. This system can include two basic parts, which are: - Speaker segmentation - Speaker clustering - The work of the segmentation part[6] is to divide the speech signal into segments that only contain the speech of one speaker. In the clustering stage [7], the speech parts related to a speaker are identified and categorized and a single label is assigned to it. This article is used in many speech applications that are related to speech recognition or indexing[8] in an environment where several speakers may speak, such as a meeting, conference, news, and the like. This work can not only help advanced speech recognition systems to improve the results of group recognition, but also help them in identifying and transcribing conversations. as information varies depending on who utters the spoken words. Within the speech technologies, the broad topic of acoustic indexing studies the classification of sounds into different classes/sources. Algorithms used for acoustic indexing worry about the correct classification of the sounds, but not necessarily about the correct separation of them when more than one exist in the same audio segment. These purely classification techniques have sometimes been called audio clustering, which benefits from the broad topic of clustering, well studies in many areas. When multiple sounds appear in the same audio signal one must turn his attention to techniques called as audio diarization to process them. These can include particular speakers, music, background noise sources.

When the possible classes correspond to the different speakers in a recording these techniques

are called speaker diarization. Speaker diarization can be defined in terms of being a subtype of audio diarization, where the speech segments of the signal are broken into the different speakers. They aim at answering the question "Who spoke when?" given an audio signal. Algorithms doing speaker diarization need to locate each speaker turn and assign them to the appropriate speaker cluster. The output of the system is a set of segments with a unique ID assigned to each person who intervenes in the recording.