Systems and processes for intelligent device identification are provided. In one example process, audio input may be sampled with a microphone at each of two or more of the plurality of electronic devices. A first electronic device of the plurality of electronic devices for determining a task associated with sampled audio input may be identified. The process may determine the task based on the sampled audio input with the first electronic device and identify identifying a second electronic device of the plurality of electronic devices for performing the task. The task be performed with the second electronic device. The second electronic device is not the first electronic device in some examples.

The disclosure provides a speech processing method and a device thereof. The method includes: acquiring a speech sampling signal frame in a mixed-excitation linear prediction (MELP) speech coding system and estimating signal quality of the speech sampling signal frame; determining, based on the signal quality, a specific linear prediction coding (LPC) order used by an LPC circuit; controlling the LPC circuit to convert the speech sampling signal frame into a line spectrum pair parameter based on the specific LPC order; replacing a speech signal spectrum of the speech sampling signal frame with the line spectrum pair parameter to generate a predicted speech signal; and performing a speech coding operation and a signal synthesizing operation of the MELP speech coding system based on the predicted speech signal.

The disclosure provides a speech processing method and a device thereof. The method includes: acquiring a speech sampling signal frame in a mixed-excitation linear prediction (MELP) speech coding system and estimating signal quality of the speech sampling signal frame; determining, based on the signal quality, a specific linear prediction coding (LPC) order used by an LPC circuit; controlling the LPC circuit to convert the speech sampling signal frame into a line spectrum pair parameter based on the specific LPC order; replacing a speech signal spectrum of the speech sampling signal frame with the line spectrum pair parameter to generate a predicted speech signal; and performing a speech coding operation and a signal synthesizing operation of the MELP speech coding system based on the predicted speech signal.

There are provided mechanisms for transmitting a representation of a speech signal to a second terminal device. A method is performed by a first terminal device. The method includes obtaining a speech signal to be transmitted to the second terminal device. The method includes obtaining an indication of whether to, when encoding the speech signal into the representation, convert the speech signal to a text signal or not before transmission to the second terminal device. The indication is based on information of local ambient background noise at the first terminal device and of current network conditions between the first terminal device and the second terminal device. The method includes encoding the speech signal into the representation of the speech signal as determined by the indication. The method includes transmitting the representation of the speech signal towards the second terminal device.

There are provided mechanisms for transmitting a representation of a speech signal to a second terminal device. A method is performed by a first terminal device. The method includes obtaining a speech signal to be transmitted to the second terminal device. The method includes obtaining an indication of whether to, when encoding the speech signal into the representation, convert the speech signal to a text signal or not before transmission to the second terminal device. The indication is based on information of local ambient background noise at the first terminal device and of current network conditions between the first terminal device and the second terminal device. The method includes encoding the speech signal into the representation of the speech signal as determined by the indication. The method includes transmitting the representation of the speech signal towards the second terminal device.

There is provided mechanisms for handling poor audio quality. A method is performed by a receiving terminal device. The method comprises obtaining an indication of poor audio quality of incoming audio at the receiving terminal device. The incoming audio originates from a transmitting terminal device. The method comprises initiating text conversion of the incoming audio. The method comprises receiving text resulting from automatic speech recognition having been applied to the incoming audio. The method comprises providing a representation of the text to a user interface of the receiving terminal device.

There is provided mechanisms for handling poor audio quality. A method is performed by a receiving terminal device. The method comprises obtaining an indication of poor audio quality of incoming audio at the receiving terminal device. The incoming audio originates from a transmitting terminal device. The method comprises initiating text conversion of the incoming audio. The method comprises receiving text resulting from automatic speech recognition having been applied to the incoming audio. The method comprises providing a representation of the text to a user interface of the receiving terminal device.

A computer-implemented method of generating speech training data is proposed. The method may include generating, at a processor, a recording script corresponding to particular text. The method may also include generating, at the processor, recorded data by performing recording by a speaker based on the recording script. The method may further include labeling, at the processor, the recorded data. Various embodiments can generate a large amount of speech training data for training an artificial neural network model while minimizing a worker's inconvenience and time consumption.

A computer-implemented method of generating speech training data is proposed. The method may include generating, at a processor, a recording script corresponding to particular text. The method may also include generating, at the processor, recorded data by performing recording by a speaker based on the recording script. The method may further include labeling, at the processor, the recorded data. Various embodiments can generate a large amount of speech training data for training an artificial neural network model while minimizing a worker's inconvenience and time consumption.

First and second speech data can be received from respective first and second devices. The first and second speech data can be determined to be from a same dialog. A link can be generated based on the dialog.