  The process of human speech production involves coordinated respiratory
action to elicit acoustic speech signals. Typically, speech is produced when
air is forced from the lungs and is modulated by the vocal tract, where such
actions are interspersed by moments of breathing in air (inhalation) to refill
the lungs again. Respiratory rate (RR) is a vital metric that is used to assess
the overall health, fitness, and general well-being of an individual. Existing
approaches to measure RR (number of breaths one takes in a minute) are
performed using specialized equipment or training. Studies have demonstrated
that machine learning algorithms can be used to estimate RR using bio-sensor
signals as input. Speech-based estimation of RR can offer an effective approach
to measure the vital metric without requiring any specialized equipment or
sensors. This work investigates a machine learning based approach to estimate
RR from speech segments obtained from subjects speaking to a close-talking
microphone device. Data were collected from N=26 individuals, where the
groundtruth RR was obtained through commercial grade chest-belts and then
manually corrected for any errors. A convolutional long-short term memory
network (Conv-LSTM) is proposed to estimate respiration time-series data from
the speech signal. We demonstrate that the use of pre-trained representations
obtained from a foundation model, such as Wav2Vec2, can be used to estimate
respiration-time-series with low root-mean-squared error and high correlation
coefficient, when compared with the baseline. The model-driven time series can
be used to estimate $RR$ with a low mean absolute error (MAE) ~ 1.6
breaths/min.
