![]() Under this new partnership, Allison Voice is now directly available through Digium sales channels, supported directly by Digium, and integrated directly into the Asterisk platform. Through previous arrangements between Digium and Cepstral, the Allison Voice software was available to the Asterisk community as a third-party add-on. With it, users of the Asterisk platform can create customized IVRs that mix static prompts in Smith’s voice with informational messages custom prompts emails dynamic information, such as appointment or account data or any other text files, also featuring Smith’s voice.įor Digium, "the Allison Voice has brand appeal," says Jim Webster, director of technology partnerships at Digium, "because is the first voice people hear in an Astrerisk system." Smith’s is the default voice of the Asterisk system, with millions of installed copies, and can be heard on systems for Verizon, Qwest, Cingular, Bank of America, Marriott Hotels, eBay, 3M, and other high-profile companies. The Allison Voice TTS software application, created by Cepstral, features the voice of popular Canadian voiceover artist Allison Smith. Through a new partnership between Digium, creator of the popular Asterisk open-source telephony platform, and text-to-speech vendor Cepstral, Digium is now selling and supporting Cepstral’s Allison Voice TTS software directly as part of its own interactive voice response platform offering. Speech Technology Magazine's Reference Guideĭigium Offers Cepstral “Allison Voice” TTS for Asterisk Telephony Systems. ![]() Speech Technology Case Studies and Market Spotlights.Translation/Globalization/Localization Services.Speaker Identification and Authentication.Natural Language, Machine/Cognitive Learning. ![]() Able to classify AI synthesised speech from Human speech with accurracy of 98.5 % on test data. Found the Highest Cross Validation Acuraacy in Quadratic SVM. Trained the various models of Machine Learning like Quadratic and Linear SVM, KNN, Logistic Regression etc. Then used those features to identify AI synthesised speech and Human speech using Machine Learning. This works include extraction of the features from various audio samples based on Spectral and Cepstral Analysis. ![]() We integrate both these analyses and propose a machine learning model to detect AI synthesized speech. Also, Cepstral analysis revealed a durable power component in human speech that is missing for a synthesized speech. Higher-order statistics have less correlation for human speech in comparison to a synthesized speech. We propose an approach to distinguish human speech from AI synthesized speech exploiting the Bi-spectral and Cepstral analysis. Validating the authenticity of a speech is one of the primary problems of digital audio forensics. It seems exciting to have a handful of tools for easy editing and manipulation, but it raises alarming concerns that can propagate as speech clones, duplicates, or maybe deep fakes. Key Terms : Machine Learning, Audio Synthesis, Multimedia Forensics, AI Synthesised Audio, Human Voice, Spectral Analyisis.ĭigital technology has made possible unimaginable applications come true. ![]() All experiments and model traing is performed in MATLAB Language and Software. Human Voice vs AI Synthesised Voice Classification using Machine Learning. ![]()
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