Using voice recognition algorithms to reduce noise in financial time-series?
First, could you please explain what you mean by "noise" in financial time series? Are you talking about extracting a trend from time series?

In itself, speech recognition consists in converting a signal into a higher-level semantic, symbolic representation of this signal. It does not achieve any denoising, so it would be surprising to use speech recognition techniques to perform any modification of a signal.

Maybe you have something else in mind?

Anyway, a speech recognition system is made of the following layers:


A feature extraction module, converting chunks of audio signal into a vector of features capturing a representation of sound which is somewhat noise-robust and very roughly pitch invariant.
An acoustic model (usually, GMMs) computing the match (a likelihood score) between the audio feature vector and a very large set of phone models.
Optionally, an adaptation module "closing the loop" between the first two steps - learning a better parametrization that makes the extracted features conform more to the known model.
Then a hierarchy of HMM-like models (phones models, word pronunciation models, language model) - all combined into a single giant finite state transducer.


The first step doesn't make sense at all for financial data, since the parametrization used for speech recognition is deeply rooted into the way speech sounds are produced, and perceived by the human ear.

There could be financial applications of the kind of tools (Hidden Markov Models and their variants) used for the following steps... But then, it's a very well known artillery that is not specific to speech recognition and they are indeed widely used in time series analysis - for segmentation, change detection, detection of outliers, or automatic labeling of time series with the kind of "patterns" used by technical analysts.