From these sessions, the researchers obtained six possible affective states, which they modeled and use as labels along with labels for speaking rate (here defined as the number of syllables per second in each utterance). The speakers, all of whom were trained voice actors, were prompted to read text snippets with varying levels of valence (emotions like sadness or happiness) and arousal (excitement or energy). (This system uses WaveRNN.)Īn annotated data set comprising 72,405 roughly 5-second recordings from 40 English speakers, amounting to 45 hours of audio, was used to train the system.

Similar to Google’s Tacotron 2, a text-to-speech (TTS) system that generates natural-sounding speech from raw transcripts, the new system can produce visual representations of frequencies called spectrograms by training a second model such as DeepMind’s WaveNet to act as a vocoder, a voice codec that analyzes and synthesizes voice data. The researchers’ system taps a trained generative model that can synthesize acoustic features from text. MetaBeat will bring together thought leaders to give guidance on how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, CA. The researchers said that just 3 minutes of data allowed for control of speech rate and extrapolation outside data seen during training - a result the researchers claim beat out state-of-the-art baselines. They report that using 30 minutes of labeled data allowed for a “significant degree” of control over speech rate, valence, and arousal, and that affect accuracy didn’t degrade noticeably with at least 10% of labeled data. The researchers trained the system for 300,000 steps across 32 of Google’s custom-designed tensor processing units (TPUs), a scale of compute exceeding that used in previous work. The work from Google and the University of College London addresses this limitation. The method achieved good results with only 5% of labeled data, but it couldn’t handle speech samples with varying prosody (i.e., intonation, tone, stress, and rhythm) and fixed emotion. A previous study investigated the use of so-called style tokens (which represented different categories of emotion) to control speech affect.

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