Abstract: The present invention provides a method for OSA (Obstructive Sleep Apnea) severity classification by using recording-based Peripheral Oxygen Saturation Signal. The major feature of the present invention emphasizes on using a recording-based Peripheral Oxygen Saturation Signal (SpO2 signal) as an input, which is different from the deep learning-based prior art of using segment-based signals as an input to a model, and the segment-based signals has only two classification results, i.e. normal or apnea.

Abstract: The present invention provides a method for OSA (Obstructive Sleep Apnea) severity detection using recording-based electrocardiography (ECG) Signal. The major feature of the present invention emphasizes on using a recording-based ECG Signal as an input, which is different from the deep learning-based prior art of using segment-based signals as an input to a model, and the segment-based signals has only two classification results, i.e. normal or apnea. The present invention provides a method for a model to detect and output directly a value of apnea-hypopnea index (AHI) for the OSA Severity.

Abstract: The present invention provides a caption service system for remote speech recognition, which provides caption service for the hearing impaired. This system includes a speaker and a live broadcast equipment at A, a listener-typist and a computer at B, a hearing impaired and a live screen at C, and an automatic speech recognition (ASR) caption server at D. Connect the live broadcast equipment, the computer, the live screen and the ASR caption server with a network. The speaker's audio is sent to the automatic speech recognition (ASR) caption server to be converted into text, which is corrected by the listener-typist, and then the text caption is sent to the live screen of the hearing impaired together with the speaker's video and audio, so that the hearing impaired can see the text caption spoken by the speaker.

Abstract: The present invention provides a speech recognition system for teaching assistance, which provides caption service for the hearing impaired. This system includes a speaker and a automatic speech recognition (ASR) classroom server, a listener-typist and a computer, a hearing impaired and a live screen, all are in the same classroom. Connect the ASR classroom server, the computer and the live screen with a local area network. The speaker's audio is sent to the ASR classroom server by a microphone for being converted into text caption, and then the text caption is sent to the live screen of the hearing impaired together with the speaker's audio so that the hearing impaired can read the text caption spoken by the speaker. The text caption can be corrected by the listener-typist to make it completely correct.

Abstract: The present invention provides a caption service system for remote speech recognition, which provides caption service for the hearing impaired. This system includes a speaker and a live broadcast equipment at A, a listener-typist and a computer at B, a hearing impaired and a live screen at C, and an automatic speech recognition (ASR) caption server at D. Connect the live broadcast equipment, the computer, the live screen and the ASR caption server with a network. The speaker's audio is sent to the automatic speech recognition (ASR) caption server to be converted into text, which is corrected by the listener-typist, and then the text caption is sent to the live screen of the hearing impaired together with the speaker's video and audio, so that the hearing impaired can see the text caption spoken by the speaker.

Abstract: The present invention provides a method for updating speech recognition system through air. Client ASR servers connect with a central ASR cloud server through Internet. New version of ASR system is stored in the central ASR cloud server for being selected and downloaded by the client ASR servers for using.

Abstract: The present invention provides a method for generating caption file through URL of an AV platform. By using various websites (such as YouTube, Instagram, Facebook, Twitter) for being inputted with the URL of a desired AV Platform and downloading a required AV file and inputting to an ASR (Automatic Speech Recognition) server according to the present invention. A speech recognition system in the ASR server can abstract an audio file from the AV file for a system operation to get a required caption file. Artificial Neural Networks are used in the present invention.

Abstract: The present invention provides a system for selecting a special speech recognition model through a general model of an AI speech recognition system for users to select an appropriate model. In addition to the AI speech recognition server of a general model, the present invention additionally prepares speech models in various fields, such as sports event model, financial news model, and game live model. Different users can choose different speech models according to their needs or fields, and they can get better services respectively. If the different users have no special choice, the AI speech recognition server of the general model provides speech recognition services for the different users.

Abstract: A method is disclosed. The proposed method includes: providing an initial speech corpus including plural utterances; based on a condition of maximum a posteriori (MAP), according to respective sequences of syllable duration, syllable duration prosodic state, syllable tone, base-syllable type, and break type of the kth utterance, using a probability of an ISR of the kth utterance xk to estimate an estimated value {circumflex over (x)}k of the xk; and through the MAP condition, according to respective sequences of syllable duration, syllable duration prosodic state, syllable tone, base-syllable type, and break type of the given lth breath group/prosodic phrase group (BG/PG) of the kth utterance, using a probability of an ISR of the lth BG/PG of the kth utterance xk,l to estimate an estimated value {circumflex over (x)}k,l of the xk,l wherein the {circumflex over (x)}k,l is the estimated value of local ISR, and a mean of a prior probability model of the {circumflex over (x)}k,l is the {circumflex over (x)}k.

Abstract: A method is disclosed. The proposed method includes: providing an initial speech corpus including plural utterances; based on a condition of maximum a posteriori (MAP), according to respective sequences of syllable duration, syllable duration prosodic state, syllable tone, base-syllable type, and break type of the kth utterance, using a probability of an ISR of the kth utterance xk to estimate an estimated value {circumflex over (x)}k of the xk; and through the MAP condition, according to respective sequences of syllable duration, syllable duration prosodic state, syllable tone, base-syllable type, and break type of the given lth breath group/prosodic phrase group (BG/PG) of the kth utterance, using a probability of an ISR of the lth BG/PG of the kth utterance xk,l to estimate an estimated value {circumflex over (x)}k,l of the xk,l wherein the {circumflex over (x)}k,l is the estimated value of local ISR, and a mean of a prior probability model of the {circumflex over (x)}k,l is the {circumflex over (x)}k.

Abstract: A speech-synthesizing device includes a hierarchical prosodic module, a prosody-analyzing device, and a prosody-synthesizing unit. The hierarchical prosodic module generates at least a first hierarchical prosodic model. The prosody-analyzing device receives a low-level linguistic feature, a high-level linguistic feature and a first prosodic feature, and generates at least a prosodic tag based on the low-level linguistic feature, the high-level linguistic feature, the first prosodic feature and the first hierarchical prosodic model. The prosody-synthesizing unit synthesizes a second prosodic feature based on the hierarchical prosodic module, the low-level linguistic feature and the prosodic tag.

Abstract: A Chinese speech recognition system and method is disclosed. Firstly, a speech signal is received and recognized to output a word lattice. Next, the word lattice is received, and word arcs of the word lattice are rescored and reranked with a prosodic break model, a prosodic state model, a syllable prosodic-acoustic model, a syllable-juncture prosodic-acoustic model and a factored language model, so as to output a language tag, a prosodic tag and a phonetic segmentation tag, which correspond to the speech signal. The present invention performs rescoring in a two-stage way to promote the recognition rate of basic speech information and labels the language tag, prosodic tag and phonetic segmentation tag to provide the prosodic structure and language information for the rear-stage voice conversion and voice synthesis.

Abstract: A speech-synthesizing device includes a hierarchical prosodic module, a prosody-analyzing device, and a prosody-synthesizing unit. The hierarchical prosodic module generates at least a first hierarchical prosodic model. The prosody-analyzing device receives a low-level linguistic feature, a high-level linguistic feature and a first prosodic feature, and generates at least a prosodic tag based on the low-level linguistic feature, the high-level linguistic feature, the first prosodic feature and the first hierarchical prosodic model. The prosody-synthesizing unit synthesizes a second prosodic feature based on the hierarchical prosodic module, the low-level linguistic feature and the prosodic tag.

Abstract: A Chinese speech recognition system and method is disclosed. Firstly, a speech signal is received and recognized to output a word lattice. Next, the word lattice is received, and word arcs of the word lattice are rescored and reranked with a prosodic break model, a prosodic state model, a syllable prosodic-acoustic model, a syllable-juncture prosodic-acoustic model and a factored language model, so as to output a language tag, a prosodic tag and a phonetic segmentation tag, which correspond to the speech signal. The present invention performs rescoring in a two-stage way to promote the recognition rate of basic speech information and labels the language tag, prosodic tag and phonetic segmentation tag to provide the prosodic structure and language information for the rear-stage voice conversion and voice synthesis.