AI News, Using 3D Convolutional Neural Networks for Speaker Verification - Official Project Page

Using 3D Convolutional Neural Networks for Speaker Verification - Official Project Page

This repository contains the code release for our paper titled as 'Text-Independent Speaker

code is aimed to provide the implementation for Speaker Verification (SR) by using 3D convolutional neural networks following

If you used this code, please kindly consider citing the following paper: For running a demo, after forking the repository, run the following scrit:

We leveraged 3D convolutional architecture for creating the speaker model in order to simultaneously capturing

In the enrollment stage, the trained network is utilized to directly create a speaker

models based on averaging the extracted features from utterances of the speaker, which

In our paper, we propose the implementation of 3D-CNNs for direct speaker model creation in

The MFCC features can be used as the data representation of the spoken utterances at the frame level.

This operation disturbs the locality property and is in contrast with the local characteristics of the convolutional operations.

sound sample, 80 temporal feature sets (each forms a

of ζ × 80 × 40 which is formed from 80 input frames

The code architecture part has been heavily inspired by Slim and Slim image classification library.

If you used this code please kindly cite the following paper: The license is as follows: Please refer to LICENSE file for further detail.

Using 3D Convolutional Neural Networks for Speaker Verification - Official Project Page

This repository contains the code release for our paper titled as 'Text-Independent Speaker

code is aimed to provide the implementation for Speaker Verification (SR) by using 3D convolutional neural networks following

If you used this code, please kindly consider citing the following paper: For running a demo, after forking the repository, run the following scrit:

We leveraged 3D convolutional architecture for creating the speaker model in order to simultaneously capturing

In the enrollment stage, the trained network is utilized to directly create a speaker

models based on averaging the extracted features from utterances of the speaker, which

In our paper, we propose the implementation of 3D-CNNs for direct speaker model creation in

The MFCC features can be used as the data representation of the spoken utterances at the frame level.

This operation disturbs the locality property and is in contrast with the local characteristics of the convolutional operations.

sound sample, 80 temporal feature sets (each forms a

of ζ × 80 × 40 which is formed from 80 input frames

The code architecture part has been heavily inspired by Slim and Slim image classification library.

If you used this code please kindly cite the following paper: The license is as follows: Please refer to LICENSE file for further detail.

Code-switching

In linguistics, code-switching occurs when a speaker alternates between two or more languages, or language varieties, in the context of a single conversation.

Code-switching is distinct from other language contact phenomena, such as borrowing, pidgins and creoles, loan translation (calques), and language transfer (language interference).

Some scholars use either term to denote the same practice, while others apply code-mixing to denote the formal linguistic properties of language-contact phenomena and code-switching to denote the actual, spoken usages by multilingual persons.[4][5][6]

Some discourse analysts, including conversation analyst Peter Auer, suggest that code-switching does not simply reflect social situations, but that it is a means to create social situations.[19][20][21]

It posits that language users are rational and choose to speak a language that clearly marks their rights and obligations, relative to other speakers, in the conversation and its setting.[22]

Scholars of conversation analysis such as Peter Auer and Li Wei argue that the social motivation behind code-switching lies in the way code-switching is structured and managed in conversational interaction;

That is, whatever language a speaker chooses to use for a conversational turn, or part of a turn, impacts the subsequent choices of language by the speaker as well as the hearer.

Rather than focusing on the social values inherent in the languages the speaker chooses ('brought-along meaning'), the analysis concentrates on the meaning that the act of code-switching itself creates ('brought-about meaning').[16][23]

The communication accommodation theory (CAT), developed by Howard Giles, professor of communication at the University of California, Santa Barbara, seeks to explain the cognitive reasons for code-switching, and other changes in speech, as a person either emphasizes or minimizes the social differences between himself and the other person(s) in conversation.

In contrast to convergence, speakers might also engage in divergent speech, in which an individual person emphasizes the social distance between himself and other speakers by using speech with linguistic features characteristic of his own group.

In studying the syntactic and morphological patterns of language alternation, linguists have postulated specific grammatical rules and specific syntactic boundaries for where code-switching might occur.

The equivalence constraint predicts that switches occur only at points where the surface structures of the languages coincide, or between sentence elements that are normally ordered in the same way by each individual grammar.[32]

The casa white is ruled out by the equivalence constraint because it does not obey the syntactic rules of English, and the blanca house is ruled out because it does not follow the syntactic rules of Spanish.[32]

The sentence: 'The students had visto la película italiana' ('The students had seen the Italian movie') does not occur in Spanish-English code-switching, yet the free-morpheme constraint would seem to posit that it can.[35]

The equivalence constraint would also rule out switches that occur commonly in languages, as when Hindi postpositional phrases are switched with English prepositional phrases like in the sentence: 'John gave a book ek larakii ko' ('John gave a book to a girl').

The phrase ek larakii ko is literally translated as a girl to, making it ungrammatical in English, and yet this is a sentence that occurs in English-Hindi code-switching despite the requirements of the equivalence constraint.[32]

The Matrix Language Hypothesis states that those grammatical procedures in the central structure in the language production system which account for the surface structure of the Matrix Language + Embedded Language constituent (linguistics) are only Matrix Language–based procedures.

According to the Blocking Hypothesis, in Matrix Language + Embedded Language constituents, a blocking filter blocks any Embedded Language content morpheme which is not congruent with the Matrix Language with respect to three levels of abstraction regarding subcategorization.

This approach views explicit reference to code-switching in grammatical analysis as tautological, and seeks to explain specific instances of grammaticality in terms of the unique contributions of the grammatical properties of the languages involved.

Rather than posit constraints specific to language alternation, as in traditional work in the field, MacSwan advocates that mixed utterances be analyzed with a focus on the specific and unique linguistic contributions of each language found in a mixed utterance.

Because these analyses draw on the full range of linguistic theory, and each data set presents its own unique challenges, a much broader understanding of linguistics is generally needed to understand and participate in this style of codeswitching research.

These constraints, among others like the Matrix Language-Frame model, are controversial among linguists positing alternative theories, as they are seen to claim universality and make general predictions based upon specific presumptions about the nature of syntax.[4][40]

Selvamani also uses the word tsé ('you know', contraction of tu sais) and the expression je me ferrai pas poigné ('I will not be handled'), which are not standard French but are typical of the working-class Montreal dialect Joual.[42]

Code Switching: Definition, Types and Examples

There are a number of possible reasons for switching from one language to another, and these will now be considered, as presented by Crystal (1987).

Others in the elevator who do not speak the same language would be excluded from the conversation and a degree of comfort would exist amongst the speakers in the knowledge that not all those present in the elevator are listening to their conversation.

The socio-linguistic benefits have also been identified as a means of communicating solidarity, or affiliation to a particular social group, whereby code switching should be viewed from the perspective of providing a linguistic advantage rather than an obstruction to communication.

Further, code switching allows a speaker to convey attitude and other emotives using a method available to those who are bilingual and again serves to advantage the speaker, much like bolding or underlining in a text document to emphasize points.

Pragmatics

Pragmatics is a subfield of linguistics and semiotics that studies the ways in which context contributes to meaning.

Pragmatics encompasses speech act theory, conversational implicature, talk in interaction and other approaches to language behavior in philosophy, sociology, linguistics and anthropology.[1]

Unlike semantics, which examines meaning that is conventional or 'coded' in a given language, pragmatics studies how the transmission of meaning depends not only on structural and linguistic knowledge (e.g., grammar, lexicon, etc.) of the speaker and listener, but also on the context of the utterance,[2]

In this respect, pragmatics explains how language users are able to overcome apparent ambiguity, since meaning relies on the manner, place, time, etc.

Similarly, the sentence 'Sherlock saw the man with binoculars' could mean that Sherlock observed the man by using binoculars, or it could mean that Sherlock observed a man who was holding binoculars (syntactic ambiguity).[8]

As defined in linguistics, a sentence is an abstract entity — a string of words divorced from non-linguistic context — as opposed to an utterance, which is a concrete example of a speech act in a specific context.

The more closely conscious subjects stick to common words, idioms, phrasings, and topics, the more easily others can surmise their meaning;

One way to define the relationship is by placing signs in two categories: referential indexical signs, also called 'shifters,' and pure indexical signs.

The referential aspect of its meaning would be '1st person singular' while the indexical aspect would be the person who is speaking (refer above for definitions of semantico-referential and indexical meaning).

If two people were in a room and one of them wanted to refer to a characteristic of a chair in the room he would say 'this chair has four legs' instead of 'a chair has four legs.'

The former relies on context (indexical and referential meaning) by referring to a chair specifically in the room at that moment while the latter is independent of the context (semantico-referential meaning), meaning the concept chair.

Michael Silverstein has argued that 'nonreferential' or 'pure' indices do not contribute to an utterance's referential meaning but instead 'signal some particular value of one or more contextual variables.'[14]

For instance, when a couple has been arguing and the husband says to his wife that he accepts her apology even though she has offered nothing approaching an apology, his assertion is infelicitous—because she has made neither expression of regret nor request for forgiveness, there exists none to accept, and thus no act of accepting can possibly happen.

Roman Jakobson, expanding on the work of Karl Bühler, described six 'constitutive factors' of a speech event, each of which represents the privileging of a corresponding function, and only one of which is the referential (which corresponds to the context of the speech event).

Because pragmatics describes generally the forces in play for a given utterance, it includes the study of power, gender, race, identity, and their interactions with individual speech acts.

Morris, pragmatics tries to understand the relationship between signs and their users, while semantics tends to focus on the actual objects or ideas to which a word refers, and syntax (or 'syntactics') examines relationships among signs or symbols.

This process, integral to the science of Natural language processing, involves providing a computer system with some database of knowledge related to a topic and a series of algorithms which control how the system responds to incoming data, using contextual knowledge to more accurately approximate natural human language and information processing abilities.

proper logical theory of formal pragmatics has been developed by Carlo Dalla Pozza, according to which it is possible to connect classical semantics (treating propositional contents as true or false) and intuitionistic semantics (dealing with illocutionary forces).

In Excitable Speech she extends her theory of performativity to hate speech and censorship, arguing that censorship necessarily strengthens any discourse it tries to suppress and therefore, since the state has sole power to define hate speech legally, it is the state that makes hate speech performative.

Personalized Hey Siri

Apple introduced the “Hey Siri” feature with the iPhone 6 (iOS 8).

allows users to invoke Siri without having to press the home button.

When a user says, “Hey Siri, how is the weather today?” the phone

wakes up upon hearing “Hey Siri” and processes the rest of the utterance

The feature’s ability to listen continuously for the “Hey Siri” trigger phrase lets users access Siri in situations where their hands might be otherwise occupied, such as while driving or cooking, as well as in situations

to set a timer while putting a turkey into the oven.

and iPad used a low-power and always-on processor for continuous

The more general, speaker-independent problem of “Hey Siri” detection is also

We describe our early modeling efforts using deep neural

networks and set the stage for the improvements described in a subsequent

users would invoke Siri using the home button and inadvertently

In particular, our early offline experiments showed, for a reasonable

- 1) when the primary user says a similar phrase, 2) when other users

say “Hey Siri,” and 3) when other users say a similar phrase.

The overall goal of speaker recognition (SR) is to ascertain the identity

speaking,” as opposed to the problem of speech recognition, which aims

to ascertain “what was spoken.” SR performed using a phrase known

We measure the performance of a speaker recognition system as a combination

total number of true “Hey Siri” instances spoken by the target user.

alarm errors as imposter accepts (IA) and avoid confusing such errors

step will have to contend with false accept errors made by the

The application of a speaker recognition system involves a two-step process:

and decides whether to accept that utterance as belonging to the existing

enrollment, a user is asked to say the target trigger phrase a few

times, and the on-device speaker recognition system trains a PHS speaker

created using clean speech, but real-world situations are almost never

This brings to bear the notion of implicit enrollment, in which a speaker

profile is created over a period of time using the utterances spoken

the resulting profile will be corrupted and not faithfully represent the primary users’

voice or falsely accept other imposters’ voices (or both!) and the

enrollment phrases requested from the user are, in order: 1.

“Hey Siri, it’s me.” This variety of utterances also helps to inform users of the different ways

in which this feature can be utilized (1-shot and 2-shot mode), and everyone

we imagine a future without any explicit enrollment step in which users

simply begin using the “Hey Siri” feature from an empty profile, which

in two steps, as shown in the expanded version of the block at the

we attempt to transform the speech vector in a way that focuses on speaker-specific

in varying environments (e.g., kitchen, car, cafe, etc.) and modes of

vocalization (e.g., groggy morning voice, normal voice, raised voice, etc.).

On each “Hey Siri”-enabled device, we store a user profile consisting of a

five vectors after the explicit enrollment process.

for every incoming test utterance and compute its cosine score (i.e.,

than a pre-determined threshold (\lambda), then the device wakes up and processes the subsequent command.

implicit enrollment process, we add the latest accepted speaker vector

are deployed via an over-the-air update, each user profile can

means into a 28 * 13 = 364-dimensional vector.

This approach closely resembles existing work in the research field, where

800 production users with more than 100 utterances each and produced

The transform was further improved by using explicit enrollment data, enhancing

higher order cepstral coefficients can capture more speaker-specific

speech vector containing 26 * 17 = 442 dimensions.

(i.e., 1x100S) followed by a 100-neuron hidden layer with a linear

activation, and a softmax layer with 16000 output nodes.

is trained using the speech vector as an input and the corresponding

trained, the last layer (softmax) is removed and the output of the linear

followed by the 100-neuron linear layer obtained the best results.

larger network’s increase in number of parameters by applying 8-bit quantization

speaker recognition system via a single equal error rate (EER) value; this

Personalized Hey Siri performance (end-to-end) Table 1 shows the EER’s obtained using the three different speaker transforms

1a show that speaker recognition performance improves noticeably with

an improved front end (speech vector) and the non-linear modeling brought

on by a neural network (speaker vectors), while the third row demonstrates

the authors explored similar approaches on a different dataset and obtained

of the complete feature using various speaker transforms.

is performed using 2800 hours of negative (non-trigger) data from

podcasts and other sources, as well as positive trigger data from 150

(large room) and noisy (car, wind) environments still remain more

In our subsequent work [2], we demonstrate success in the form

of multi-style training, in which a subset of the training data is augmented

At its core, the purpose of the “Hey Siri” feature is to enable users to make

the utterance (e.g., “…, how’s the weather today?”) in the form of text-independent

curriculum learning with a recurrent neural network architecture (specifically

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