Emotion Detection and Recognition market to reach USD 22.65 billion by 2020 This study has been done on a global level broadly covering four regions, namely, North America, Europe, APAC, Middle East and RoW, and the market is projected to grow from USD 5.66 billion in 2015 to USD 22.65 billion by 2020, at a CAGR of 31.9% during the period. The market is being driven by factors such as increased focus on affective computing, business intelligence, and growing amount of spatial data as well as prompt availability of analytical tools. "Law Enforcement, Surveillance, and Monitoring areas are projected to showcase robust growth in the emotion detection and recognition market" The defense and security agencies require emotion detection technology for surveillance and monitoring purposes. Major implementation of this technology has already been done in the areas of military services such as lie detectors and polygraph tests. The emotion detection technology helps in matching the records in real-time and detecting the stress levels of a criminal.
Abstract: In this work, we propose a non-autoregressive seq2seq model that converts text to spectrogram. It is fully convolutional and obtains about 17.5 times speed-up over Deep Voice 3 at synthesis while maintaining comparable speech quality using a WaveNet vocoder. Interestingly, it has even fewer attention errors than the autoregressive model on the challenging test sentences. Furthermore, we build the first fully parallel neural text-to- speech system by applying the inverse autoregressive flow (IAF) as the parallel neural vocoder. Our system can synthesize speech from text through a single feed-forward pass.
Microsoft has reached a milestone in text-to-speech synthesis with a production system that uses deep neural networks to make the voices of computers nearly indistinguishable from recordings of people. With the human-like natural prosody and clear articulation of words, Neural TTS has significantly reduced listening fatigue when you interact with AI systems. Our team demonstrated our neural-network powered text-to-speech capability at the Microsoft Ignite conference in Orlando, Florida, this week. The capability is currently available in preview through Azure Cognitive Services Speech Services. Neural text-to-speech can be used to make interactions with chatbots and virtual assistants more natural and engaging, convert digital texts such as e-books into audiobooks and enhance in-car navigation systems.