This paper publishes an open and free speech database thuyg20 sre and a benchmark for uyghur speaker recognition. Speaker recognition is identifying an individual speaker from a set of potential speakers while speaker verification is confirming a speakers identity as the true speaker or as an imposter who may be trying to. Speaker recognition is a process that enables machines to understand and interpret the human speech by making use of certain algorithms and verifies the authenticity of a speaker with the help of a database 14. Efficient speaker identification from speech transmitted on. In this paper we discuss properties of speech databases used for speaker recognition research and evaluation, and we characterize some popular standard databases. Speaker recognition and verification is essential in confirming identity in numerous realworld applications. This challenging database was collected to encourage researchers to develop novel algorithms for benchmarking speaker recognition technology and is available at. The paper presents a new database called elsdsr dedicated to speaker recognition applications. Audio databases the following databases are made available to the speech community for research purposes only. For more information about pricing, please visit the cognitive services pricing page. During the project period, an english language speech database for speaker recognition elsdsr was built. The latest challenge website can be found here and the latest workshop website can be found here. Lee has written two books on speech recognition and more than 60 papers in computer science.
Speaker recognition means, comprising a database configured to store speaker information for a target speaker. General information librispeech is a corpus of read speech, based on librivoxs public domain audio books. They are nowadays widely used in several application. The database consists of recordings of 299 speakers, with an average of eight different sessions per. The sitw database was collected under different challenging conditions for open source media. A multispectral data fusion approach to speaker recognition. Chime this is a noisy speech recognition challenge dataset 4gb in size. The database contains voice messages from 22 speakers. The second part is the ddhmm speaker recognition performed on the survived speakers after pruning. Citeseerx document details isaac councill, lee giles, pradeep teregowda.
Pdf databases, features and classifiers for speech. For berlin database, all of the classifiers achieve an accuracy of 83% when a speaker normalization sn and a feature selection fs are applied to the features. This is the case with, for instance, the speechdat and broertjespolyphone databases. The first result is an overview of 36 existing databases that has been used in speaker. Speaker recognition api is available as a standalone service. Ep2499637a1 speaker recognition from telephone calls. This article presents an overview of the polycost database dedicated to speaker recognition applications over the telephone network. Input audio of the unknown speaker is paired against a group of selected speakers and in the case there is a match found, the speaker s identity is returned. It is unique in its clear explanations of mathematical. Speaker recognition is the identification of a person from characteristics of voices. You get a solid background in voice recognition technology to help you make informed decisions on which voice recognitionbased software to use in your company or organization. Speaker identification and verification by combining mfcc and. The input speech signal is taken from the database. We host a voxceleb speaker recognition challenge voxsrc at interspeech every year.
The pool of speakers consists of 300 participants 143 female and 157 male speakers. Speaker recognition has been a widely used field topic of speech. We aim to meet the data requirement for farfield microphone array based speaker verification since most of the publicly available databases are single channel closetalking and textindependent. Speaker identification and verification by combining mfcc. The speakers in the wild sitw speaker recognition database. Related fields of research are speaker recognition and speaker segmentation 45, 160. Speaker recognition known as voiceprint recognition in industry is the process of. Few research has been conducted on uyghur speaker recognition. By using a popular or readily available database results can be directly compared with those previously published by others. Databases, features and classifiers for speech emotion recognition. Numerous and frequentlyupdated resource results are available from this search. In conventional speaker recognition methods based on melfrequency cepstral coefficients mfccs, phase information has hitherto been ignored. Since then over 70 research sites have participated in our evaluations. Identification is the process of determining from which of the registered speakers a given utterance comes.
Chandra 2 department of computer science, bharathiar university, coimbatore, india suji. This paper presents a farfield textdependent speaker verification database named himia. Applications of speaker identification are authentication in safety systems and user recognition in dialog systems. Pdf databases, features and classifiers for speech emotion. Each year new researchers in industry and universities are encouraged to participate. The speakers in the wild sitw speaker recognition database contains handannotated speech samples from opensource media for the purpose of benchmarking textindependent speaker recognition technology on single and multi speaker audio acquired across unconstrained or wild conditions. Evaluation of a speaker identification system with and. Although this book originally aims the field of speaker recognition, i found it equally valuable as an introduction to speech recognition, given the numerous.
Speaker recognition an overview sciencedirect topics. A large handannotated realcondition database for textindependent speaker recognition. We conclude the paper with discussion on future directions. Timit ntimit timit texas instruments massachusetts institute of technology. It can be used for authentication, surveillance, forensic speaker recognition and a. Speaker recognition can be divided into two specific tasks. Other interesting aspects of inter speaker variability is the inclusion of close relatives among speakers, and of human or technical mimicry. Feature vectors extracted in the feature extraction module are veri. Oct 30, 2015 an openfree database and benchmark for uyghur speaker recognition abstract. It consists of 392 hours of conversational telephone speech in english, arabic, mandarin chinese, russian and spanish and associated english transcripts used as training data in. Verification is the process of accepting or rejecting the identity claimed by a speaker. To study the impact of different variabilities on the speaker recognition task, the speech data is collected in multisensor, multilingual, multistyle and multienvironment conditions. An overview of textindependent speaker recognition. Przybocki national institute of standards and technology gaithersburg, md 20899 usa alvin.
An emerging technology, speaker recognition is becoming wellknown for providing voice authentication over the telephone for helpdesks, call centres and other enterprise businesses for business process automation. Divided by use case, it includes data on speaker identification to. Automatic speech emotion recognition using machine. This dataset contains hundreds of thousands of voice samples for voice recognition. Its purpose is to enable the training and testing of automatic speech recognitionasr systems. In this paper, we propose a phase information extraction method that normalizes the change variation in the phase according to the frame position of the input speech and combines the phase information with mfccs in textindependent speaker. Efficient speaker identification from speech transmitted on bluetooth ali khalil on. The present final report gives an overview of the activities in this wg, and presents its main results. An openfree database and benchmark for uyghur speaker. Working group wg 2 of the cost250 action speaker recognition in telephony has dealt with databases for speaker recognition. Gmm based speaker recognition on readily available databases. Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation recognizing when the same.
To build the corpus, the content came from user submitted blog posts, old movies, books, and other public speech. Collaboration between universities and industries is also welcomed. Ser reported the best recognition rate of 94% on the spanish database using rnn classifier without speaker normalization sn and with feature selection fs. Automatic speech emotion recognition using machine learning. Speaker recognition is the process of automatically recognizing who is speaking by using the speaker specific information included in speech waves to verify identities being claimed by people accessing systems. The various technologies used to process and store voice prints include frequency estimation, hidden markov models, gaussian mixture models, pattern matching algorithms, neural networks, matrix representation, vector quantization and decision trees. Automatic speaker recognition is the use of a machine to recognize a person from a spoken phrase. New features to improve speaker recognition efficiency with.
Together with alex waibel, another carnegie mellon researcher, lee edited readings in speech recognition. We have selected 5 different us presidents and enroled them to the service using one of the speeches they gave. While speech recognition focuses on converting speech spoken words to digital data, we can also use fragments to identify the person who is speaking. Other interesting aspects of interspeaker variability is the inclusion of close relatives among speakers, and of human or technical mimicry. Speaker recognition deals with the identification of the speaker in an audio stream. The mathworks web site is the official matlab site. Among the limited works, researchers usually collect small speech databases and publish results based on their own private data. Speaker recognition, textindependence, feature extraction, statistical models, discriminative models, supervectors, intersession variability compensation 1. Looking at the sample project and reading the apis documentation, i understood that the recognition should be done sending a wav file to the service, which goes against my goal of doing it real time. The first result is an overview of 36 existing databases that has been used in speaker recognition research. Index termsspeaker recognition, gaussian mixture model, feature extraction, expectation maximization, timit database. Speaker recognition is a pattern recognition problem.
Pdf fundamentals of speaker recognition researchgate. Input audio of the unknown speaker is paired against a group of selected speakers and in the case there is a match found, the speakers identity is returned. Identifying speakers with voice recognition python deep. A telephonespeech database for speaker recognition. The technical problems are rigorously defined, and a complete picture is made of the relevance of the discussed algorithms and their usage in building a comprehensive. Speaker recognition can be classified into identification and verification. The data has been sourced from audio books from the librivox project and is 60 gb. Multivariability speech database for robust speaker. Introduction feature extraction is the key part of the frontend process in speaker identification systems.
A new database for speaker recognition dtu research database. Heres a scientific look at computergenerated speech verification and identification its underlying technology, practical applications, and future direction. Im trying to build an application that solves the problem of speaker diarization by using the microsoft cognitive speaker recognition apis. Phoneme recognition on the timit database intechopen. By adding the speaker pruning part, the system recognition accuracy was increased 9. The given input speech signal is a noisy signal which has been taken from the database. English spoken by nonnative speakers, a single session of sentence reading and relatively extensive speech samples suitable for learning person specific speech characteristics. Speaker recognition free download as powerpoint presentation.
The speakers in the wild sitw speaker recognition challenge 2016. To validate our architecture, we took standardized data from the english language speech database for speaker recognition elsdsr 40. This is a speaker recognition challenge held on the voxceleb datasets. The term voice recognition can refer to speaker recognition or speech recognition. Fundamentals of speaker recognition introduces speaker identification, speaker verification, speaker audio event classification, speaker detection, speaker tracking and more. Introduction feature extraction is the key part of the front. More than 151 h of speech data were recorded using mobile devices. Introduction speaker recognition refers to recognizing persons from their. This book presents a study for speaker recognition rates for speech transmitted through bluetooth channel as a degraded speech signals. The dataset contains real simulated and clean voice recordings. New features to improve speaker recognition efficiency. Speaker recognition in a multispeaker environment alvin f martin, mark a.
The 77 best speech recognition books recommended by jakob nielsen, such as. Pdf an emerging technology, speaker recognition is becoming wellknown for. Speaker recognition or broadly speech recognition has been an active area of research for the past two decades. The relevant research on timit phone recognition over the past years will be addressed by trying to cover this wide range of technologies. Speaker recognition cluster analysis applied mathematics. An expanded list of links to matlab educational resources on the web including tutorials and teaching examples. Gmm based speaker recognition on readily available. A fundamental english database based on audiobook recordings for textindependent speaker recognition. His doctoral dissertation was published in 1988 as a kluwer monograph, automatic speech recognition.
May 12, 2016 speaker recognition api is available as a standalone service. Speaker recognition can be classified as speaker identification and speaker verification, as shown in figure 7. Voxsrc consists of an online challenge and an accompanying workshop at interspeech. Voxceleb is a largescale speaker identification dataset. Spoken commands dataset a large database of free audio samples 10m words, a test bed for voice activity detection algorithms and for recognition of syllables singleword commands. Common voice common voice is mozillas initiative to help teach machines how real people speak. To build the corpus, the content came from user submitted blog posts, old movies, books. Speaker recognition systems have been studied for many years. The api can be used to determine the identity of an unknown speaker.
Us20120232900a1 speaker recognition from telephone calls. Nist has been coordinating speaker recognition evaluations since 1996. As the human auditory system can sensitively perceive the pitch changes in the speech, the speech information obtained by the mfcc with the pitch, can dynamically construct a set of melfilters. It includes over 500 hours of speech recordings alongside speaker demographics. The database is based on the thuyg20 speech corpus we recently released, and the benchmark involves recognition tasks with various trainingenrollmenttest conditions. Every individual has different characteristics when speaking, caused by differences in anatomy and behavioral patterns. Reliable information about the coronavirus covid19 is available from the world health organization current situation, international travel. The most commonly used feature for speech and speaker recognition that facilitates better speech as well as speaker characteristics is mfcc 14. Speaker recognition using universal background model on yoho. For the application of speaker recognition there exists many readily available databases such as yoho, timit, and andosl. An emerging technology, speaker recognition is becoming wellknown for. Large amount of opensource data extracted from youtube using computer vision techniques for speaker recongition and speaker diarization. It can be used for authentication, surveillance, forensic speaker recognition and a number of related activities. Speaker recognition using universal background model on.
Efficient speaker identification from speech transmitted. In this work we built a lstm based speaker recognition system on a dataset collected from cousera lectures. Oclcs webjunction has pulled together information and resources to assist library staff as they consider how to handle coronavirus. The database contains recordings of 340 people in rooms designed for the farfield scenario. For berlin database, all of the classifiers achieve an accuracy of 83% when a speaker normalization sn and a.