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Sentiment Analysis With Fully Supervised Speaker Diarization
Abstract
Speaker diarization with emotions analysis has been one of the most difficult tasks of the Machine Learning and Artificial Intelligence genre. But, this problem is now resolved by some well-known tech companies like Google but it depends upon whether they have open-sourced their work and not, it made a specific population aware of it. In this paper, RNN approach would be discussed in order to provide better accuracy for the sequential data and its advantages over the clustering process, why it best to work with RNN if your data has labels, Using Deep Affects API We will give the actual visualization of the whole conversation between the people, what were their emotions during their phrases what was their overall sentiment of the conversation involving plenty of emotions in the trained data it gives better accuracy and even shows it on the visualization page.
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Speaker clustering is a crucial step for speaker diarization. The short duration of speech segments in telephone speech dialogue and the absence of prior information on the number of clusters dramatically increase the difficulty of this problem in diarizing spontaneous telephone speech conversations. We propose a simple iterative Mean Shift algorithm based on the cosine distance to perform speaker clustering under these conditions. Two variants of the cosine distance Mean Shift are compared in an exhaustive practical study. We report state of the art results as measured by the Diarization Error Rate and the Number of Detected Speakers on the LDC CallHome telephone corpus.
Speaker diarization via unsupervised i-vector clustering has gained popularity in recent years. In this approach, i-vectors are extracted from short clips of speech segmented from a larger multi-speaker conversation and organized into speaker clusters, typically according to their cosine score. In this paper, we propose a system that incorporates probabilistic linear discriminant analysis (PLDA) for i-vector scoring, a method already frequently utilized in speaker recognition tasks, and uses unsupervised calibration of the PLDA scores to determine the clustering stopping criterion. We also demonstrate that denser sampling in the i-vector space with overlapping temporal segments provides a gain in the diarization task. We test our system on the CALLHOME conversational telephone speech corpus, which includes multiple languages and a varying number of speakers, and we show that PLDA scoring outperforms the same system with cosine scoring, and that overlapping segments reduce diarization error rate (DER) as well.
In this paper we present a data-driven, integrated approach to speaker
verification, which maps a test utterance and a few reference utterances
directly to a single score for verification and jointly optimizes the system's
components using the same evaluation protocol and metric as at test time. Such
an approach will result in simple and efficient systems, requiring little
domain-specific knowledge and making few model assumptions. We implement the
idea by formulating the problem as a single neural network architecture,
including the estimation of a speaker model on only a few utterances, and
evaluate it on our internal "Ok Google" benchmark for text-dependent speaker
verification. The proposed approach appears to be very effective for big data
applications like ours that require highly accurate, easy-to-maintain systems
with a small footprint.
We develop the distance dependent Chinese restaurant process, a flxible class of distributions over partitions that allows for dependencies between the elements. This class can be used to model many kinds of dependencies between data in infinit clustering models, including dependencies arising from time, space, and network connectivity. We examine the properties of the distance dependent CRP, discuss its connections to Bayesian nonparametric mixture models, and derive a Gibbs sampler for both fully observed and latent mixture settings. We study its empirical performance with three text corpora. We show that relaxing the assumption of exchangeability with distance dependent CRPs can provide a better fi to sequential data and network data. We also show that the distance dependent CRP representation of the traditional CRP mixture leads to a faster-mixing Gibbs sampling algorithm than the one based on the original formulation.
In this paper we investigate the use of deep neural networks (DNNs) for a small footprint text-dependent speaker verification task. At development stage, a DNN is trained to classify speakers at the frame-level. During speaker enrollment, the trained DNN is used to extract speaker specific features from the last hidden layer. The average of these speaker features, or d-vector, is taken as the speaker model. At evaluation stage, a d-vector is extracted for each utterance and compared to the enrolled speaker model to make a verification decision. Experimental results show the DNN based speaker verification system achieves good performance compared to a popular i-vector system on a small footprint text-dependent speaker verification task. In addition, the DNN based system is more robust to additive noise and outperforms the i-vector system at low False Rejection operating points. Finally the combined system outperforms the i-vector system by 14% and 25% relative in equal error rate (EER) for clean and noisy conditions respectively.
A five-year interdisciplinary effort by speech scientists and computer scientists has demonstrated the feasibility of programming a computer system to “understand” connected speech, i.e., translate it into operational form and respond accordingly. An operational system (HARPY) accepts speech from five speakers, interprets a 1000-word vocabulary, and attains 91 percent sentence accuracy. This Steering Committee summary report describes the project history, problem, goals, and results.
Audio diarization is the process of annotating an input audio channel with information that attributes (possibly overlapping) temporal regions of signal energy to their specific sources. These sources can include particular speakers, music, background noise sources, and other signal source/channel characteristics. Diarization can be used for helping speech recognition, facilitating the searching and indexing of audio archives, and increasing the richness of automatic transcriptions, making them more readable. In this paper, we provide an overview of the approaches currently used in a key area of audio diarization, namely speaker diarization, and discuss their relative merits and limitations. Performances using the different techniques are compared within the framework of the speaker diarization task in the DARPA EARS Rich Transcription evaluations. We also look at how the techniques are being introduced into real broadcast news systems and their portability to other domains and tasks such as meetings and speaker verification
Locally-connected and convolutional neural networks for small footprint speaker recognition
- Ignacio Yu-Hsin Chen
- Tara N Lopez-Moreno
- Sainath
- Raziel Mirkóvisontai
- Carolina Alvarez
- Parada
Yu-hsin Chen, Ignacio Lopez-Moreno, Tara N Sainath, MirkóVisontai, Raziel Alvarez, and
Carolina Parada, "Locally-connected and convolutional neural networks for small footprint speaker recognition," in Sixteenth Annual Conference of the International Speech
Communication Association, 2015.