Since considerable human input is required in fsasr and some in fasr, the problem. Approaching human listener accuracy with modern speaker. Hasr human assisted speaker recognition began addressing this question a 2010 pilot test hasr included two tests. Enhancing mimicry attacks using automatic target speaker selection. The separate pilot evaluation of human assisted speaker recognition hasr for systems including human expertise in their processing is described in section 6. Hasr systems may use human listeners, machines, or both participation open to all who might be interested the hasr task. Automatic speaker recognition algorithms in python. Nist sre included a human assisted speaker recognition.
Speaker recognition can be classified as speaker identification and speaker verification, as shown in figure 7. We recorded 6 naive mimics for whom we select target celebrities from. The nist series of speaker recognition evaluations sres have, since 1996, evaluated automatic systems for speaker recognition. Inria, france abstract we consider technology assisted mimicry attacks in the context of automatic speaker veri. The human assisted speaker recognition hasr system is an expertbased process adopted from general forensic phonetics methodology, combined with output from the m1tll gmm lfa fred2 automatic system. Speaker recognition introduction measurement of speaker characteristics construction of speaker models decision and performance applications this lecture is based on rosenberg et al. Introduction there has been considerable interest recently on comparing the performance of humans and machines on speaker recognition, due to the human assisted speaker recognition hasr test. The following multistep process is used with the aid of the super phonetic annotation and analysis tool 7, 8. Exercises for forensic semiautomatic and automatic speaker recognition. Unfortunately, as soon as speech sequence information is. The speaker recognition process based on a speech signal is treated as one of the most exciting technologies of human recognition orsag 2010. Speaker recognition, human assisted, nist hasr 2012, plda system 1. Chandra 2 department of computer science, bharathiar university, coimbatore, india suji.
Participants were invited to complete the trials in one of two small subsets of the full set of trials included in the core test of the main automatic system evaluation. Similarly, voiceactivated virtual assistants on smart. Speaker recognition technical university of denmark. Textindependent, automatic speaker recognition system evaluation with males speaking both arabic and english thesis directed by professor catalin grigoras abstract automatic speaker recognition is an important key to speaker identification in media forensics and with the increase of cultures mixing, theres an increase in bilingual. Verification is the process of accepting or rejecting the identity claimed by a speaker. Introduction recognizing people by their voices in a variety of settings and under unfavorable conditions is a task that the speech community has studied extensively for many decades. Usssmitll 2010 human assisted speaker recognition conference paper pdf available in acoustics, speech, and signal processing, 1988. For instance, it is now possible to determine the gender of the speaker with accuracy that matches the human perception of genders. Human speakers is still building and shipping speakers during this public health crisis more information. Przybocki national institute of standards and technology gaithersburg, md 20899 usa alvin. The 2010 evaluation sre10 also included a test of human assisted speaker.
Identification is the process of determining from which of the registered speakers a given utterance comes. We consider technologyassisted mimicry attacks in the context of automatic speaker veri. The robot is able to estimate the sound source position and send only nonvoice sounds along with location data to a human caregiver for recognition and labelling. The 2010 evaluation sre10 also included a test of human assisted speaker recognition hasr, in which systems based, in whole or in part, on human. Merging human and automatic system decisions to improve speaker recognition performance rosa gonzalez hautam. John godfrey, us department of defense, united states. Manual vs assisted transcription of prepared and spontaneous speech. Abstract our paper focuses on the gain which can be achieved on human transcription of spontaneous and prepared speech, by using the assistance. Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation recognizing when the same.
In this work we built a lstm based speaker recognition system on a dataset collected from cousera lectures. Keywords human activity recognition, active and assisted living, sensor networks, smart systems. Index terms speaker recognition, human perception, human assisted speaker recognition 1. We use asv itself to select targeted speakers to be attacked by human based mimicry. The trends in recent sre participation are addressed in section 7, and a brief summary of sre10 performance results is. Human assisted speaker recognition using forced alignments. During the project period, an english language speech database for speaker recognition elsdsr was built. Since nist introduced the hasr test as a pilot evaluation there has been some research in the area, most of it related to the way humans can complement automatic speaker recognition systems as in 1 or. Pdf usssmitll 2010 human assisted speaker recognition. Investigatory voice biometrics committee report 3 summary the idea of automated and semiautomated humanassisted speaker recognition for forensic. Human experts trained in forensic speaker recognition can perform this task even better by examining a set of acoustic, prosodic, and linguistic characteristics of speech in a general approach. This way of dealing with the problem, configures a human assisted approach. The power of smartphones feng xia 1, chinghsien 1hsu 2, xiaojing liu 1, haifeng liu, fangwei ding 1, wei zhang 1 1school of software, dalian university of technology, dalian 116620, china 2department of computer science and information engineering, chung hua university, taiwan abstract.
The second part is the ddhmm speaker recognition performed on the survived speakers after pruning. Forensic speaker identification is a decisionmaking process which determines whether a given utterance has been spoken by a particular person or not. Denver pet partners joined forces with american humane in 2007, and is now the practicing arm of american humane s humananimal bond division. Speaker recognition an overview sciencedirect topics. Smartphones have been shipped with multiple wireless network interfaces in. And yet, when we call and speak with a computer at an automated customer service center we dont have the same experience. Speech processing and the basic components of automatic speaker recognition systems are shown and design tradeoffs are discussed. Craig greenberg, alvin martin, national institute of standards and technology, united states.
Can we use speaker recognition technology to attack itself. It is an important topic in speech signal processing and has a variety of applications, especially in security systems. Speaker recognition for forensic applications this work was sponsored under air force contract fa872105c0002. Cognitive effects of robotassisted language learning on. Conventional nist sre uses too many trials comparisons for human processing. Tap the photo to reach more detailed information about the product, and the parts i make to repairupgrade it. By adding the speaker pruning part, the system recognition accuracy was increased 9.
About speaker recognition techology applied biometrics. Human assisted speaker recognition hasr in nist sre10. We consider technologyassisted mimicry attacks in the context of automatic speaker verification asv. Recently, some good advancement has been made in that field.
It can be used for authentication, surveillance, forensic speaker recognition and a. Automatic speaker verification asv assisted mimicry at tack. Speaker recognition in a multispeaker environment alvin f martin, mark a. We show that naive listeners vary substantially in their performance, but that an aggregation of listener responses can achieve performance similar to that of expert forensic examiners. In addition to improving speaker recognition methodology, it is also of great in. Therefore, voice recognition fits within the category of behavioral biometrics. We use asv itself to select targeted speakers to be attacked by humanbased mimicry. Of particular recent research interest is how to combine human expertise and. Humanassisted sound event recognition for home service.
The 2010 evaluation sre10 also included a test of human assisted speaker recognition hasr, in which systems based, in whole or. Humanassisted sound event recognition contains three functions. Voice controlled devices also rely heavily on speaker recognition. Opinions, interpretations, conclusions, and recommendations are those of the authors and are not necessarily endorsed by the united states government. To achieve this goal, a number of speech and voice features are evaluated. Methodological guidelines for best practice in forensic. Human assisted speaker recognition in nist sre10 isca speech. Speaker recognition introduction speaker, or voice, recognition is a biometric modality that uses an individuals voice for recognition purposes. Have you called a family member or friend on the phone and within just a couple of words they know exactly who you are and what you are planning to talk about. Asr is done by extracting mfccs and lpcs from each speaker and then forming a speakerspecific codebook of the same by using vector quantization i like to think of it as a fancy.
In other words the human has to show some of hisher speaking behavior. Speaker recognition or broadly speech recognition has been an active area of research for the past two decades. Modelling, feature extraction and effects of clinical environment a thesis submitted in fulfillment of the requirements for the degree of doctor of philosophy sheeraz memon b. The 2010 evaluation sre10 also included a test of human assisted speaker recognition hasr, in which systems based, in whole or in part, on human expertise were evaluated. The results are encouraging within the resolving power of the evaluation, which was limited to enable reasonable levels of human effort.
Speaker recognition using deep belief networks cs 229 fall 2012. The recording of the human voice for speaker recognition requires a human to say something. For speaker recognition, for example, the gmm is directly used as a universal background model for the speech feature distribution pooled from all speakers. The process of speaker identification results in either positive identification, i. Communication systems and networks school of electrical and computer engineering. The 2010 sre evaluation sre10 included a test of human assisted speaker recognition hasr, in which systems based, in whole or in part, on human expertise were evaluated. Confidence estimation for speaker and language recognition corpora and tools for system development and evaluation lowresource lightly supervised speaker and language recognition speaker synthesis and transformation human and humanassisted recognition of speaker and language spoofingpresentation attacks. Ai assisted language learning computer engineering. Speaker recognition is the identification of a person from characteristics of voices.
This repository contains python programs that can be used for automatic speaker recognition. Finally, in 2010 and 2012, a human assisted speaker recognition hasr task was proposed, where any combinations of machines, naive listeners or human experts were allowed in order to perform speaker detection over a manual selection of especially difficult trials from the core condition greenberg et. Introduction measurement of speaker characteristics. The article concludes with a comparison of the existing methodologies which, when applied to realworld scenarios, allow to formulate research questions for future approaches. Given two different speech segments, determine whether they are both spoken by the same speaker hasr1 hasr2.
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