Speech Synthesis TRIZ Cases

A machine-learned audio inpainting model uses text transcripts to generate replacement speech content.

A diffusion-based speech generative model converts source audio to target voice using independent prosody embeddings.

A neural network synthesizes new speaker voices by predicting vectors from pre-trained models using adversarial training.

Predicting message template cadence allows selecting matching voice units and synthesizing missing parts, resolving discontinuous pitch at data boundaries.

A text-to-speech normalizer converts peculiar expressions into standard forms to enable accurate speech synthesis.

Fusing language speaker and emotion data within single utterances builds training datasets for speech synthesis models.

Audio synthesizer generates personalized voice output streams using extracted user characteristics and applies acoustic watermarks to identify machine-made audio.

A multi-lingual speech synthesizer assembles phoneme sequences from separate language databases to generate natural-sounding voice messages.

A foreign language learning apparatus uses text-to-speech waveforms to evaluate user voice input accuracy.

A speech synthesis system uses a neural network to determine target acoustic features from linguistic inputs for sample selection.

A text-to-speech system creates a synthetic voice by measuring frequency, timbre, and rhythm from a recorded vocal sample.

A text-to-speech system applies emotion-specific adjustments to a neutral acoustic trajectory for expressive output.

Polynomial expansion coefficients model pitch near syllable centers to resolve discontinuities in unvoiced segments and silence.

A speech synthesis system generates diverse audio candidates for semantic content and selects the optimal segment to match a predetermined emotion type.

Modifying pitch and formant parameters differentiates similar voices without requiring multiple audio channels.

A machine learning model assigns sentence IDs to text segments, enabling context-aware generation of acoustic codes for natural speech rhythm and tone.

A hybrid speech synthesis system combines rule-based and concatenative methods using phone-and-transition units for natural voice output.

A device extracts speech emotional indicators and timing data to generate artificial speech.

Electronic devices cache synthesized speech data locally to reuse prompts without repeated server requests.

A distributed voice data collection system gathers speech samples from multiple donors to build personalized text-to-speech models.

A parallel wave generation method using Gaussian inverse autoregressive flow for speech synthesis.

A text-to-speech engine selects optimal phonetic transcriptions using a cost function to match speaker pronunciation styles.

Templates organize phoneme sequences into coherent text documents, resolving the conflict between comprehensive coverage and reading fluency.

Segmenting speech into voiced, unvoiced, and pause units enables variable compression ratios that halve storage space while maintaining audio quality.

A reference system clusters word forms by terminal endings and stress positions to enable rapid determination of new word accents.

A sound processing method transforms spectrum envelope contours to generate natural singing voice signals.

Phoneme piece interpolator generates natural voice signals by resolving tone consistency issues across different pitches.

A pronunciation interface segments text strings into phonemes or syllables to generate accurate audio files.

A voice decoder reconstructs lost frames using parameters from adjacent packets to maintain speech continuity.

An audio manipulation manager transforms user voice categories to match original content creators by adjusting tone and pitch parameters.

A speech synthesis model uses embedding, synthesis, and position layers to generate audio from text.

Extracting prosodic features from speech data reduces computational costs and improves stability by avoiding extensive retraining.

A speech processing apparatus models logarithmic spectral envelopes as linear combinations of local domain bases to generate efficient parameters.

A text-to-speech system generates feature vectors from segmented text portions to synthesize continuous audio output.

A scalable speech coding apparatus synthesizes channel prediction signals from a generated monaural signal.

Automated text-to-speech voice development system using machine learning algorithms to modify conversion rules and speech segments based on user feedback.

A text-to-speech system adapts synthesized speech using pre-stored sender voice characteristics to mimic natural prosody.

A sequence-to-sequence model encodes acoustic features into context vectors to condition a spectrogram estimator for text-to-speech synthesis.

A deep neural network generates a continuous acoustic space model to synthesize speech with selected attributes like emotions and accents.

A speech synthesis model extracts voice feature vectors using potential energy-based loss functions to generate high-quality audio output.

A voice processing method extracts timbre from user audio to train conversion models.

A model estimates distances between prosodic contours and text attributes to select appropriate acoustic features.

Weighted summation of quasi and random excitation signals creates natural speech-to-noise transitions, resolving listener discomfort in low-bandwidth codecs.

A speech quality estimation method extracts source pitchmarks and maps them to target pitchmarks for objective degradation calculation.

A generative audio model concatenates input modal labels to synthesize high-quality signals.

A speech synthesis system generates continuous parameters to mimic natural speech flow.

A text-to-audio conversion system maps characters to stored audio data for playback.

An AI audio synthesis method uses Gaussian attention to align phoneme sequences with hidden states for accurate signal generation.

A speech editing apparatus stores representative waveforms for similar units to reduce data quantity.