Abstract: A device for computing an inner product includes a data memory, an inverted index memory (IIM), a weight mapping table, a controller, a pre-accumulator, and a multiplier-accumulate (MAC) module. The data memory stores data groups. Each data group includes data values. The IIM stores a data address and a corresponding weight index value of each data group in the data memory. The weight mapping table stores a weight value corresponding to the weight index value. The controller and the IIM drive the data memory to sequentially output the data values of the data groups and drive the mapping table to sequentially output weight values. The pre-accumulator accumulates the data values of each data group to generate accumulation values. The MAC module computes the accumulation and the weight value that correspond to each data group based on a distributive law, thereby generating an inner product value.

Abstract: A personalized voice conversion system includes a cloud server and an intelligent device that communicates with the cloud server. The intelligent device upstreams an original voice signal to the cloud server. The cloud server converts the original voice signal into an intelligible voice signal based on an intelligible voice conversion model. The intelligent device downloads and plays the intelligible voice signal. Based on the original voice signal and the corresponding intelligible voice signal, the cloud server and the intelligent device train an off-line voice conversion model provided to the intelligent device. When the intelligent device stops communicating with the cloud server, the intelligent device converts a new original voice signal into a new intelligible voice signal based on the off-line voice conversion model and plays the new intelligible voice signal.

Abstract: The present application discloses a Method for converting vibration to voice frequency wirelessly and a method thereof. By sensing a first vibration variation data and a voice frequency variation data of a vocal vibration part in a first sensing period, a voice frequency reference data is obtained from the voice frequency variation data and the first vibration result. A second vibration result is obtained at a second sensing period for converting to a voice frequency output signal, and the voice frequency output signal is used to output as a voice signal corresponding to the voice frequency various result. Thus, the present application provides a voice signal close to a human voice.

Abstract: A device for computing an inner product includes an index unit, a storage operation unit, a redundant to 2's complement (RTC) converter, a mapping table, and a multiplier-accumulate (MAC) module. The index unit, storing index values, is coupled to word lines. The storage operation unit includes the word lines and bit lines and stores data values. The mapping table stores coefficients corresponding to the index values. The index unit enables the word line according to a count value and the index value, such that the storage operation unit accumulates the data values corresponding to the bit lines and the enabled word line, thereby generating accumulation results. The RTC converter converts the accumulation results into a total data value in 2's complement format. The MAC module operates based on the total data value and the coefficient to generate an inner product value.

Abstract: A personalized voice conversion system includes a cloud server and an intelligent device that communicates with the cloud server. The intelligent device upstreams an original voice signal to the cloud server. The cloud server converts the original voice signal into an intelligible voice signal based on an intelligible voice conversion model. The intelligent device downloads and plays the intelligible voice signal. Based on the original voice signal and the corresponding intelligible voice signal, the cloud server and the intelligent device train an off-line voice conversion model provided to the intelligent device. When the intelligent device stops communicating with the cloud server, the intelligent device converts a new original voice signal into a new intelligible voice signal based on the off-line voice conversion model and plays the new intelligible voice signal.

Abstract: A device and a method for clarifying dysarthria voices is disclosed. Firstly, a dysarthria voice signal is received and framed to generate dysarthria frames. Then, the dysarthria frames are received to retrieve dysarthria features. Finally, the dysarthria features are received. Without receiving phases corresponding to the dysarthria features, the dysarthria features are converted into an intelligent voice signal based on an intelligent voice conversion model. The intelligent voice conversion model is not trained by the dynamic time warping (DTW). The present invention avoids the phase distortion of the voice signal and provides more natural and clarified voices with low noise.

Abstract: A device for computing the inner product of vectors includes a vector data arranger, a vector data pre-accumulator, a number converter, and a post-accumulator. The vector data arranger stores a first vector and sequentially outputs a plurality of vector data based on the first vector. The vector data pre-accumulator stores a second vector, receives each of the vector data, and pre-accumulates the second vector, so as to generate a plurality accumulation results. The number converter and the post-accumulator receive and process all the accumulation results corresponding to each of the vector data to generate an inner product value. The present invention implements a lookup table with the vector data pre-accumulator and the number converter to increase calculation speed and reduce power consumption.

Abstract: A data feature augmentation system and method for a low-precision neural network are provided. The data feature augmentation system includes a first time difference unit. The first time difference unit includes a first sample-and-hold circuit and a subtractor. The first sample-and-hold circuit is used for receiving an input signal and obtaining a first signal according to the input signal. The first signal is related to a first leakage rate of the first sample-and-hold circuit and the first signal is the signal generated by delaying the input signal by one time unit. The subtractor is used for performing subtraction on the input signal and the first signal to obtain a time difference signal. The input signal and the time difference signal are inputted to the low-precision neural network.

Abstract: The present application discloses a system for converting vibration to voice frequency wirelessly and a method thereof. By sensing a first vibration variation data and a voice frequency variation data of a vocal vibration part in a first sensing period, a voice frequency reference data is obtained from the voice frequency variation data and the first vibration result. A second vibration result is obtained at a second sensing period for converting to a voice frequency output signal, and the voice frequency output signal is used to output as a voice signal corresponding to the voice frequency various result. Thus, the present application provides a voice signal close to a human voice.

Abstract: The present application discloses a Method for converting vibration to voice frequency wirelessly and a method thereof. By sensing a first vibration variation data and a voice frequency variation data of a vocal vibration part in a first sensing period, a voice frequency reference data is obtained from the voice frequency variation data and the first vibration result. A second vibration result is obtained at a second sensing period for converting to a voice frequency output signal, and the voice frequency output signal is used to output as a voice signal corresponding to the voice frequency various result. Thus, the present application provides a voice signal close to a human voice.

Abstract: The present application discloses a system for converting vibration to voice frequency wirelessly and a method thereof. By sensing a first vibration variation data and a voice frequency variation data of a vocal vibration part in a first sensing period, a voice frequency reference data is obtained from the voice frequency variation data and the first vibration result. A second vibration result is obtained at a second sensing period for converting to a voice frequency output signal, and the voice frequency output signal is used to output as a voice signal corresponding to the voice frequency various result. Thus, the present application provides a voice signal close to a human voice.

Abstract: A system for improving dysarthria speech intelligibility and method thereof, are provided. In the system, user only needs to provides a set of paired corpus including a reference corpus and a patient corpus, and a speech disordering module can automatically generate a new corpus completely synchronous with the reference corpus, and the new corpus can be used as a training corpus for training a dysarthria voice conversion model. The present invention does not need to use a conventional corpus alignment technology or a manual manner to perform pre-processing on the training corpus, so that manpower cost and time cost can be reduced, and synchronization of the training corpus can be ensured, thereby improving both training and conversion qualities of the voice conversion model.

Abstract: A variable-latency computing device includes a computing module, enabling units, a storage operation unit, and a detecting controller. The enabling units are divided into at least two groups. The storage operation unit includes word lines and bit lines. The enabling units enable the word line. The storage operation unit accumulates the data values corresponding to the bit lines and the enabled word line, thereby computing first accumulation values. The detecting controller controls the computing module to stop receiving the first accumulation values when the sum of the first accumulation values is higher than a threshold value and takes turns to turn off the at least two groups. The storage operation unit computes second accumulation values during different periods. The computing module receives and computes the second accumulation values corresponding to the at least two groups, so as to generate a computation value.

Abstract: A device and a method for clarifying dysarthria voices is disclosed. Firstly, a dysarthria voice signal is received and framed to generate dysarthria frames. Then, the dysarthria frames are received to retrieve dysarthria features. Finally, the dysarthria features are received. Without receiving phases corresponding to the dysarthria features, the dysarthria features are converted into an intelligent voice signal based on an intelligent voice conversion model. The intelligent voice conversion model is not trained by the dynamic time warping (DTW). The present invention avoids the phase distortion of the voice signal and provides more natural and clarified voices with low noise.

Abstract: A device for computing an inner product of vectors includes a vector data arranger, a vector data pre-accumulator, a number converter, and a post-accumulator. The vector data arranger stores a first vector and sequentially outputs a plurality of vector data based on the first vector. The vector data pre-accumulator stores a second vector, receives each of the vector data, and pre-accumulates the second vector, so as to generate a plurality accumulation results. The number converter and the post-accumulator receive and process all the accumulation results corresponding to each of the vector data to generate an inner product value. The present invention implements a lookup table with the vector data pre-accumulator and the number converter to increase calculation speed and reduce power consumption.

Abstract: A memory is suitable for performing a deep neural network operation. The memory includes: a processing unit and a weight unit. The processing unit includes a data input terminal and a data output terminal. The weight unit is configured to be coupled to the data input terminal of the processing unit. The weight unit includes an index memory and a mapping table. The index memory is configured to store multiple weight indexes. The mapping table is configured to respectively map the multiple weight indexes to multiple representative weight data.

Abstract: A device and a method for generating synchronous corpus is disclosed. Firstly, script data and a dysarthria voice signal having a dysarthria consonant signal are received and the position of the dysarthria consonant signal is detected, wherein the script data have text corresponding to the dysarthria voice signal. Then, normal phoneme data corresponding to the text are searched and the text is converted into a normal voice signal based on the normal phoneme data corresponding to the text. The dysarthria consonant signal is replaced with the normal consonant signal based on the positions of the normal consonant signal and the dysarthria consonant signal, thereby synchronously converting the dysarthria voice signal into a synthesized voice signal. The synthesized voice signal and the dysarthria voice signal are provided to train a voice conversion model, retain the timbre of the dysarthria voices and improve the communication situations.

Abstract: A device for computing an inner product includes an index unit, a storage operation unit, a redundant to 2's complement (RTC) converter, a mapping table, and a multiplier-accumulate (MAC) module. The index unit, storing index values, is coupled to word lines. The storage operation unit includes the word lines and bit lines and stores data values. The mapping table stores coefficients corresponding to the index values. The index unit enables the word line according to a count value and the index value, such that the storage operation unit accumulates the data values corresponding to the bit lines and the enabled word line, thereby generating accumulation results. The RTC converter converts the accumulation results into a total data value in 2's complement format. The MAC module operates based on the total data value and the coefficient to generate an inner product value.

Abstract: A device and a method for generating synchronous corpus is disclosed. Firstly, script data and a dysarthria voice signal having a dysarthria consonant signal are received and the position of the dysarthria consonant signal is detected, wherein the script data have text corresponding to the dysarthria voice signal. Then, normal phoneme data corresponding to the text are searched and the text is converted into a normal voice signal based on the normal phoneme data corresponding to the text. The dysarthria consonant signal is replaced with the normal consonant signal based on the positions of the normal consonant signal and the dysarthria consonant signal, thereby synchronously converting the dysarthria voice signal into a synthesized voice signal. The synthesized voice signal and the dysarthria voice signal are provided to train a voice conversion model, retain the timbre of the dysarthria voices and improve the communication situations.

Abstract: A system for improving dysarthria speech intelligibility and method thereof, are provided. In the system, user only needs to provides a set of paired corpus including a reference corpus and a patient corpus, and a speech disordering module can automatically generate a new corpus completely synchronous with the reference corpus, and the new corpus can be used as a training corpus for training a dysarthria voice conversion model. The present invention does not need to use a conventional corpus alignment technology or a manual manner to perform pre-processing on the training corpus, so that manpower cost and time cost can be reduced, and synchronization of the training corpus can be ensured, thereby improving both training and conversion qualities of the voice conversion model.