Methods of and electronic devices for determining an intent associated with a spoken user utterance

1. A method of determining an intent associated with a spoken user utterance, the spoken user utterance having been captured in a form of a digital audio signal, the method executable by a server, the method comprising:

executing, by the server, a speech-to-text analysis of the digital audio signal to determine:

at least one speech unit of the spoken user utterance, each speech unit having textual data representative of one of a word and a pause, each speech unit having a corresponding segment of the digital audio signal;

for each speech unit:

generating a respective textual feature vector by:

determining, by the server based on the respective textual data, textual features of the respective speech unit;

generating, by the server based on the respective textual features, the respective textual feature vector;

generating a respective acoustic feature vector by:

determining, by the server based on the corresponding segment of the digital audio signal, respective acoustic features of the corresponding segment of the digital audio signal;

generating, by the server based on the respective acoustic features, the respective acoustic feature vector;

generating, by the server, a respective enhanced feature vector by combining the respective acoustic feature vector and the respective textual feature vector;

employing, by the server, a neural network (NN) configured to determine the intent of the spoken user utterance by inputting into the NN the enhanced feature vectors, the NN having been trained to estimate a probability of the intent being of a given type.

2. The method of claim 1 , wherein the NN is a recurrent neural network (RNN).

3. The method of claim 1 , wherein the executing the speech-to-text analysis comprises determining:

the textual data of each speech unit; and

a time interval of the corresponding segment of the digital audio signal of each speech unit.

4. The method of claim 1 , wherein the generating the respective textual feature vector is executed by a word embedding process implemented by the server.

5. The method of claim 1 , wherein the textual feature vector of a given speech unit that is a pause is a vector with null values.

6. The method of claim 1 , wherein the acoustic features are at least some of:

volume level;

energy level;

pitch level;

harmonicity; and

tempo.

7. The method of claim 1 , wherein the determining the respective acoustic features of the corresponding segment of the digital audio signal comprises:

determining, by the server, respective acoustic features of each sub-segment of the corresponding segment of the digital audio signal by applying a sliding window, and wherein

the generating the respective acoustic feature vector comprises:

generating, by the server, respective intermediary acoustic feature vectors for each sub-segment based on the respective acoustic features; and

generating, by the server based on the respective intermediary acoustic feature vectors, the respective acoustic feature vector for the corresponding segment of the digital audio signal.

8. The method of claim 7 , wherein each sub-segment is of a pre-determined time length.

9. The method of claim 7 , wherein at least two sub-segments partially overlap.

10. The method of claim 7 , wherein the sliding window slides with a time step of another pre-determine time length.

11. The method of claim 7 , wherein the generating the respective acoustic feature vector for the corresponding segment of the digital audio signal based on the respective intermediary acoustic feature vectors comprises executing, by the server, a statistically-driven combination of the respective intermediary acoustic feature vectors.

12. The method of claim 1 , wherein the combining the respective acoustic feature vector and the respective textual feature vector comprises concatenating, by the server, the respective acoustic feature vector and the respective textual feature vector.

13. The method of claim 1 , wherein the given type is one of:

open-ended question type;

closed-ended question type;

statement type; and

exclamation type.

14. The method of claim 1 , wherein the method further comprises:

acquiring, by the server, auxiliary data generated by the NN for each inputted enhanced feature vector associated with a given word;

responsive to determining that the intent is of the given type:

executing, by the server, an auxiliary MLA for determining a target word amongst the at least one word by inputting into the auxiliary MLA the auxiliary data, the target word being indicative of a context of the spoken user utterance.

15. A server for determining an intent associated with a spoken user utterance, the spoken user utterance having been captured in a form of a digital audio signal, the server being configured to:

execute a speech-to-text analysis of the digital audio signal to determine:

at least one speech unit of the spoken user utterance, each speech unit having textual data representative of one of a word and a pause, each speech unit having a corresponding segment of the digital audio signal;

for each speech unit:

generate a respective textual feature vector by:

determining, by the server based on the respective textual data, textual features of the respective speech unit;

generating, by the server based on the respective textual features, the respective textual feature vector;

generate a respective acoustic feature vector by:

determining, by the server based on the corresponding segment of the digital audio signal, respective acoustic features of the corresponding segment of the digital audio signal;

generating, by the server based on the respective acoustic features, the respective acoustic feature vector;

generate a respective enhanced feature vector by combining the respective acoustic feature vector and the respective textual feature vector;

employ a neural network (NN) configured to determine the intent of the spoken user utterance by inputting into the NN the enhanced feature vectors, the NN having been trained to estimate a probability of the intent being of a given type.

16. The server of claim 15 , wherein the NN is a recurrent neural network (RNN).

17. The server of claim 15 , wherein the server configured to execute the speech-to-text analysis comprises the server being configured to determine:

the textual data of each speech unit; and

a time interval of the corresponding segment of the digital audio signal of each speech unit.

18. The server of claim 15 , wherein the server configured to determine the respective acoustic features of the corresponding segment of the digital audio signal comprises the server being configured to:

determine respective acoustic features of each sub-segment of the corresponding segment of the digital audio signal by applying a sliding window, and wherein

the server configured to generate the respective acoustic feature vector comprises the server being configured to:

generate respective intermediary acoustic feature vectors for each sub-segment based on the respective acoustic features; and

generate, based on the respective intermediary acoustic feature vectors, the respective acoustic feature vector for the corresponding segment of the digital audio signal.

19. The server of claim 1 , wherein the given type is one of:

open-ended question type;

closed-ended question type;

statement type; and

exclamation type.

20. The server of claim 15 , wherein the server is further configure to:

acquire auxiliary data generated by the NN for each inputted enhanced feature vector associated with a given word;

responsive to determining that the intent is of the given type:

execute an auxiliary MLA for determining a target word amongst the at least one word by inputting into the auxiliary MLA the auxiliary data, the target word being indicative of a context of the spoken user utterance.