Human voice detection software

We then connect the energy with the frequency by creating a dictionary whose keys are the absolute value of the frequency, and values are the corresponding energy at that frequency. Therefore, we sum the energy corresponding such frequencies in the time window, and we can compare it with the full sum of energies. Finally, we can define the speech ratio as being the quotien between the sum of the speech energy in the time window and the sum of the total energy.

So far, we estimated the speech ratio on the whole audio file, without using a rolling window. It is now time to combine both approaches.

You can try to play with the speech ratio threshold and the window size to see how it affects the detection. The output is interesting but would require some smoothing if we want to detect smooth regions in which a user speaks. Conclusion : In this article, we introduced the concept of voice activity detection. High-level overview It can be useful at first to give a high level overview of the classical approaches to Voice Activity Detection: Read the input file and convert is to mono Move a window of 20ms along the audio data Calculate for each window the ratio between energy of speech band and total energy for window If ratio is higher than a pre-defined threshold e.

Logically, emotionally intelligent machines will need to capture all verbal and nonverbal cues to estimate the emotional state of a person precisely, using face or voice, or both. The majority of the emotion AI developers are unanimous in declaring that the main goal of multimodal emotion recognition is to make the human-machine communication more natural. However, there is a lot of controversy around this topic. Do we really want our emotions to be machine-readable?

Today we shall focus on some positive examples of the emotional AI application. Emotional support. Nurse bots can remind older patients to take their medication and 'talk' with them every day to monitor their overall wellbeing. Mental health treatment. Emotion AI-powered chatbots can imitate a therapist or a counselor and help automate talk therapy and accessibility. There are also mood tracking apps like Woebot that help people manage mental health through short daily chat conversations, mood tracking, games, curated videos, etc.

AI as medical assistants. Emotion AI can assist doctors with diagnosis and intervention and provide better care. Entirely virtual digital humans are not designed to simply answer questions like Siri or Alexa, but are supposed to look and act like humans, show emotions, have their unique personalities, learn and have real conversations.

Understanding consumer emotional responses to brand content is crucial for reaching the marketing goals. Advertising research. Emotion is the core of effective advertising: the shift from negative to positive emotions can ultimately increase sales. Emotional AI-powered solutions like Affdex by Affectiva allow marketers to remotely measure consumer emotional responses to ads, videos, and TV shows and better evaluate their relevance. A better understanding of human emotional responses to marketing campaigns and the ability to deliver the right content through the right channel at the right time.

Cameras in public places can detect people's facial expressions and understand the general mood of the population. China, the world's largest surveillance market, is attempting to predict crimes using AI to monitor the emotional state of the citizens.

Emotion AI technologies allow companies to conduct the risk assessment and detect fraud in insurance claims in real-time, by using both voice and facial recognition. Banks and financial institutions. Credit risk assessment, fraud intention detection, immediate fact verification, risk scoring. Emotion AI can be also used to offer personalized payment experience, set up biometric face recognition ATMs, etc.

In one aspect of the method, whether or not there is a step e for mode override, step a is replaced by a step a for placing a call and upon the call being answered, prompting the called party for a response.

According to another aspect of the present invention, a duel mode IVR system is provided. The system includes a telephone interface switch, a voice recognition software instance and library, and a human voice detection software instance. The human voice detection software is called and executed during an IVR interaction with a caller only if the voice recognition software routine fails to recognize a response uttered by the caller.

In one embodiment, the telephone switch is a central office switch connected to a private branch exchange PBX. In yet another aspect of the invention, a machine readable medium is provided, the medium having thereon a set of instructions that cause a machine to perform a method including a taking a call and prompting for a voice response from the caller, b attempting to recognize the response, c upon failing to recognize the response in step b , executing a routine to detect and isolate the captured word or phrase in the response.

In one aspect in step a , the call is taken at an interactive voice response system. In one aspect, in step c , the routine includes a sub step for setting the default mode of the interactive voice response system for the rest of the interaction with the caller according to the result of a second attempt to recognize the response.

Voice telephony environment includes a public switched telephone network PSTN , a cellular network , and a communication center service point PSTN network may instead be a private telephone network instead of a public network. Switch may be an automated call distributor ACD type switch, or some other telephony network switch capable of processing and routing telephone calls.

PBX is connected to a central office switch COS located within communication center service point by a telephone trunk Cellular network may be any type of digital or analog network supporting wireless telephony without departing from the spirit and scope of the present invention.

Network includes a cell tower connected by a telephone trunk to an edge router just inside the PSTN In this example, callers a - n communicate through tower , which routes the call into the PSTN via trunk through router on to PBX Callers a - n are wired to PBX via telephone wiring.

QOS may at times be quite different for the different networks and in terms of voice quality and the amount of noise interference. Generally speaking, a wired telephone on a dedicated connection has better voice quality more of the time than, for example, a cellular telephone over a shared connection. Moreover, other factors may contribute to noise that is captured from the caller environment and carried along with the voice during a call. IVR intercepts calls from callers a - n and from callers a - n and attempts to provide service to those callers based on planned voice interaction voice application sessions with those callers.

The spoken voice is recognized by searching for the VoXML equivalent stored in the database. It is important to note herein that voice does not have to be recognized perfectly for a successful match of a caller's spoken word or phrase in database If a phrase is mostly recognized, then the software may still produce the correct system response to the voice phrase uttered by the caller.

There are several known ways including statistical pattern matching that can be used to help the voice recognition accuracy within the digital processing realm of IVR Another technique is to pool variant response words or variances of response phrases and equate them to a same value. In this example, IVR has, in addition to standard voice recognition capability, an instance of human voice detection HVD software provided thereto and executable thereon. HVD is provided to enhance the voice recognition capability of IVR by detecting in the audio captured from the caller the human voice portion of the total audio data.

Provided that the human voice data can be reliably detected then the data that does not appear to be human voice can be subtracted from an equation before standard voice recognition is employed. The method can be applied after voice recognition has failed to recognize an uttered word or phrase on a first attempt. Attempting to recognize the caller word or phrase using standard non-enhanced voice recognition software may be a default routine because under low noise circumstances there may be no need for enhancement.

However under moderate to high noise scenarios, for example, a cell phone caller in a construction zone, HVD may be helpful in isolating the human portion of the signal for presentation of only the human signal to the voice recognition software.

However, if during one round, the caller's word or phrase is not immediately recognized by the software, then instead of forcing the caller to depress a button, HVD can be used to refine the signal and a second attempt to recognize the word or phrase may be initiated. The time it takes to call the HVD routine and execute it to completion is negligible in terms of call flow. COS has a processor of suitable power and speed to run an analysis very quickly.

However, the noise causing the problem might be temporary. It will be apparent to one with skill in the art that the method of the present invention can be used to improve the interaction accuracy.

Likewise, there would be less dependence on the backup DTMF pushbutton method for the caller to insert a value. Therefore, those callers that do not have pushbutton capability on their communications devices would receive better service. If the system is implemented according to the method described, the voice application prompts would not necessarily be required to include a push button value along with the appropriate voice response word or phrase.

If the enhanced system failed to recognize the caller's word or phrase one or a specified number of times, a system prompt might be rotated in that informs the caller that voice recognition has been turned off because of the noise level or type the system is experiencing.

In this case, the subsequent prompts could be based on DTMF pushbutton only and VRT capability could be suspended for the rest of that session. One with skill in the art will recognize that the method described can be implemented in a telephony environment or in a voice over internet protocol environment where an IVR equivalent is implemented.

The IVR system itself may be caused to switch between modes in midstream based on the application of the method integrated with controlling IVR software. The HVD routine may be plugged into normal IVR programming by inserting a removable media containing the sequence start all of the tasks and the sequence end.

The routine can be inserted into any of the voice application running on the system. The following acts reflect just one of a number of possible processes that could be programmed into IVR control software and caused to run automatically as calls are processed by the IVR system. In step , the IVR boots or otherwise is brought online. In step , DTMF pushbutton recognition is activated as a backup measure.