Diagnosis and treatment of speech and language pathologies by speech to text and natural language processing

A processing unit for assessing a speech and/or lingual quality of a subject, and treating and/or diagnosing a speech and/or lingual related pathology, the processing unit comprising:

• a vocal input sub-unit executing a code configured to receive a recorded vocal sample created by a user's response to content-containing stimulus and/or to instructions related thereto;
• a speech processing sub-unit executing a code configured to extract and/or measure and/or compute at least one prosodic parameter and at least one acoustic parameter relating to the user's vocal response, wherein the at least one acoustic parameter comprises a temporal parameter which comprises the user's response time;
• a vocal-to-text sub-unit executing a code configured to transform the user's verbal response into a transformed text section;
• a comparison sub-unit executing a code configured to compare between the transformed text section and a predetermined text section, which represents the user's expected response;
• and
• an output unit executing a code configured to:
• compute an output signal indicative of at least one speech and/or lingual quality of the user, which comprises computing a multi parametric vector, based at least on data resulted from the texts comparison, the at least one extracted and/or measured and/or computed prosodic parameter and the at least one acoustic parameter;
• and
• detect a speech and/or lingual related pathology of the subject suffering from said pathology based upon the output signal wherein the speech and/or lingual related pathology is selected from a group consisting of aphasia, ALS, Parkinson, Alzheimer's and stuttering;
• wherein computing an output signal indicative of the user's speech quality, comprises applying natural language processing (NLP) algorithms,
• wherein the natural language processing (NLP) algorithms comprise machine learning algorithms,
• wherein the machine learning algorithms comprise decision trees, neural networks, statistical models, or a combination thereof, and
• wherein the statistical models comprise assigning a weight value to each of the parameters or sets of parameters and wherein the weight value is displayed on a display.

A system for assessing a speech and/or lingual quality of a subject, and treating and/or diagnosing a speech and/or lingual related pathology, the system comprising:

• a prompter configured to provide a content-containing stimulus to a user;
• a recorder configured to record a vocal sample created by the user's response to content-containing stimulus and/or to instructions related thereto;
• and
• a processing unit comprising:
• a vocal input sub-unit executing a code configured to receive a recorded vocal sample created by a user's response to content-containing stimulus and/or to instructions related thereto;
• a speech processing sub-unit executing a code configured to extract and/or measure and/or compute at least one prosodic parameter and at least one acoustic parameter relating to the user's vocal response, wherein the at least one acoustic parameter comprises a temporal parameter which comprises the user's response time;
• a vocal-to-text sub-unit executing a code configured to transform the user's verbal response into a transformed text section;
• a comparison sub-unit executing a code configured to compare between the transformed text section and a predetermined text section, which represents the user's expected response;
• and
• an output sub-unit executing a code configured to:
• compute an output signal indicative of at least one speech and/or lingual quality of the user, which comprises computing a multi parametric vector, based at least on data resulted from the texts comparison, the at least one extracted and/or measured and/or computed prosodic parameter and the at least one acoustic parameter;
• and
• detect a speech and/or lingual related pathology of the subject suffering from said pathology based upon the output signal wherein the speech and/or lingual related pathology is selected from a group consisting of aphasia, ALS, Parkinson, Alzheimer's and stuttering;
• wherein computing an output signal indicative of the user's speech quality, comprises applying natural language processing (NLP) algorithms,
• wherein the natural language processing (NLP) algorithms comprise machine learning algorithms,
• wherein the machine learning algorithms comprise decision trees, neural networks, statistical models, or a combination thereof, and
• wherein the statistical models comprise assigning a weight value to each of the parameters or sets of parameters and wherein the weight value is displayed on a display.