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Is is the app that really does know how you are feeling.

Researchers have unveiled a mood analysis app that can constantly monitor a person's speech, looking out for signs of anger or stress.

Researchers hope the technology could be used help those with anger issues and bipolar disorder, warning the user, family members and even medical staff if it detects major changes.

Angry? The Android app monitors subtle qualities of a person¿s voice during everyday phone conversations and can warn of major mood swings.

The University of Michigan team says early results from a small group of patients show its potential to monitor moods while protecting privacy.

The researchers hope the app will eventually give people with bipolar disorder and their health care teams an early warning of the changing moods that give the condition its name.

The technology could also help people with other conditions.

'These pilot study results give us preliminary proof of the concept that we can detect mood states in regular phone calls by analyzing broad features and properties of speech, without violating the privacy of those conversations,' says Zahi Karam, who led the study.

'As we collect more data the model will become better, and our ultimate goal is to be able to anticipate swings, so that it may be possible to intervene early.'

The app is called Priori, because the researchers hope it will yield a biological marker to prioritize bipolar disorder care to those who need it most urgently to stabilize their moods – especially in regions of the world with scarce mental health services.

The app allows speech to be recorded and analyzed securely, making it possible to detect changes in mood. It can alert the user, a family member and even a doctor in case of issues.

Bipolar disorder affects tens of millions of people worldwide, and can have devastating effects including suicide.

The technology and algorithms will be developed via research involving 60 American patients who receive treatment from U-M teams at the nation's first center devoted to depression and related disorders.

More patients have already started to use the app on study-provided smartphones.

As more patients volunteer, the team will continue to test and improve the technology.

Melcvin McInnis, a bipolar specialist, said 'This is tremendously exciting not only as a technical achievement, but also as an illustration of what the marriage of mental health research, engineering and innovative research funding can make possible.

'The ability to predict mood changes with sufficient advance time to intervene would be an enormously valuable biomarker for bipolar disorder.'

The app could warn people if they are becoming unusually angry by constantly monitoring their speech to look for subtle changes.

The app runs in the background on an ordinary smartphone, and automatically monitors the patients' voice patterns during any calls made as well as during weekly conversations with a member of the patient's care team.

The computer program analyzes many characteristics of the sounds – and silences – of each conversation.

It allows speech features to be recorded and analyzed securely, making it possible to detect changes in mood.

Eventually, it will include a feedback loop to the patient and his or her care team and even a chosen family member.

Only the patient's side of everyday phone calls is recorded – and the recordings themselves are encrypted and kept off-limits to the research team.

They can see only the results of computer analysis of the recordings, which are stored in secure servers that comply with patient privacy laws.

Standardized weekly mood assessments with a trained clinician provide a benchmark for the patient's mood, and are used to correlate the acoustic features of speech with their mood state.

Because other mental health conditions also cause changes in a person's voice, the same technology framework developed for bipolar disorder could prove useful in everything from schizophrenia and post-traumatic stress disorder to Parkinson's disease, the researchers say.